{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c979f719-a7bd-4392-86f9-f699220b2c93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP20</th>\n",
       "      <th>COUNTYFP20</th>\n",
       "      <th>TRACTCE20</th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>NAME20</th>\n",
       "      <th>NAMELSAD20</th>\n",
       "      <th>MTFCC20</th>\n",
       "      <th>FUNCSTAT20</th>\n",
       "      <th>ALAND20</th>\n",
       "      <th>AWATER20</th>\n",
       "      <th>...</th>\n",
       "      <th>P0050002</th>\n",
       "      <th>P0050003</th>\n",
       "      <th>P0050004</th>\n",
       "      <th>P0050005</th>\n",
       "      <th>P0050006</th>\n",
       "      <th>P0050007</th>\n",
       "      <th>P0050008</th>\n",
       "      <th>P0050009</th>\n",
       "      <th>P0050010</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>011</td>\n",
       "      <td>110403</td>\n",
       "      <td>12011110403</td>\n",
       "      <td>1104.03</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>1323099</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>POLYGON ((-80.24758 25.99480, -80.24754 25.994...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>011</td>\n",
       "      <td>060114</td>\n",
       "      <td>12011060114</td>\n",
       "      <td>601.14</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>2598912</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>POLYGON ((-80.26810 26.19368, -80.26702 26.193...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>011</td>\n",
       "      <td>060120</td>\n",
       "      <td>12011060120</td>\n",
       "      <td>601.20</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>12814719</td>\n",
       "      <td>1823779</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-80.36670 26.12828, -80.36649 26.128...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>011</td>\n",
       "      <td>110347</td>\n",
       "      <td>12011110347</td>\n",
       "      <td>1103.47</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>2846117</td>\n",
       "      <td>545293</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-80.40957 26.03541, -80.40878 26.035...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>011</td>\n",
       "      <td>020421</td>\n",
       "      <td>12011020421</td>\n",
       "      <td>204.21</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>1060862</td>\n",
       "      <td>16632</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-80.24061 26.22083, -80.24056 26.220...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 345 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP20 COUNTYFP20 TRACTCE20      GEOID20   NAME20    NAMELSAD20 MTFCC20  \\\n",
       "0        12        011    110403  12011110403  1104.03  Census Tract   G5020   \n",
       "1        12        011    060114  12011060114   601.14  Census Tract   G5020   \n",
       "2        12        011    060120  12011060120   601.20  Census Tract   G5020   \n",
       "3        12        011    110347  12011110347  1103.47  Census Tract   G5020   \n",
       "4        12        011    020421  12011020421   204.21  Census Tract   G5020   \n",
       "\n",
       "  FUNCSTAT20   ALAND20  AWATER20  ... P0050002 P0050003 P0050004 P0050005  \\\n",
       "0          S   1323099         0  ...        0        0        0        0   \n",
       "1          S   2598912         0  ...        0        0        0        0   \n",
       "2          S  12814719   1823779  ...        0        0        0        0   \n",
       "3          S   2846117    545293  ...        0        0        0        0   \n",
       "4          S   1060862     16632  ...        0        0        0        0   \n",
       "\n",
       "  P0050006 P0050007 P0050008 P0050009 P0050010  \\\n",
       "0        0       10        0        0       10   \n",
       "1        0       16        0        0       16   \n",
       "2        0        0        0        0        0   \n",
       "3        0        0        0        0        0   \n",
       "4        0        0        0        0        0   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-80.24758 25.99480, -80.24754 25.994...  \n",
       "1  POLYGON ((-80.26810 26.19368, -80.26702 26.193...  \n",
       "2  POLYGON ((-80.36670 26.12828, -80.36649 26.128...  \n",
       "3  POLYGON ((-80.40957 26.03541, -80.40878 26.035...  \n",
       "4  POLYGON ((-80.24061 26.22083, -80.24056 26.220...  \n",
       "\n",
       "[5 rows x 345 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is for working with census tracts and VTD precincts\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "#tractGeomFile = gpd.read_file(\"state_map_files/fl_pl2020_t.shp\")  #Boo!  Only has geometries.  do not use\n",
    "tractPopFile = gpd.read_file(\"state_map_files/fl_pl2020_t.dbf\") #for Texas, need only this file\n",
    "tractPopFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a7a90b04-4b48-4ab8-9195-8d953907c3fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 5160 popn tracts for FL\n"
     ]
    }
   ],
   "source": [
    "# EXTRACT TRACT GEOMETRIES AND POPULATIONS INTO LISTS, COMPUTE TRACT AREAS\n",
    "# If the population and geometry data are in one file, this should work.\n",
    "STATE = \"FL\"\n",
    "tractGeom = tractPopFile['geometry'] \n",
    "tractPop = tractPopFile['P0010001']\n",
    "tractVAP = tractPopFile['P0030001']   #NEW 3/2/22 - USE VAP\n",
    "# tractPop2 = tractPopFile['P0020001']   #not needed; confirmed that this matches P00100001 exactly\n",
    "tractHisp = tractPopFile['P0040002']   #NEW 3/2/22 - USE VAP\n",
    "tractBlack = tractPopFile['P0030004']  #NEW 3/2/22 - USE VAP\n",
    "nTracts = len(tractPop)\n",
    "print(\"there are {0} popn tracts for {1}\".format(nTracts, STATE) )\n",
    "tractArea = [0.]*nTracts\n",
    "for t in range (0,nTracts) :\n",
    "    tractArea[t] = tractGeom[t].area\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation\n",
    "tractPop = tractPop.to_numpy()  #to avoid panda overwrite grousing\n",
    "tractBlack= tractBlack.to_numpy()\n",
    "tractHisp = tractHisp.to_numpy()\n",
    "tractVAP = tractVAP.to_numpy()\n",
    "stateVAP = np.sum(tractVAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c90b3908-e2e3-46b8-98e5-ae285eca1ee3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is Hispanic population by total population FL voter-age population\n",
      "state pop= 21538187 VAP pct Hispanic is  0.24987756089773758\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Hispanic pop to total pop?\n",
    "print(\"this is Hispanic population by total population \"+STATE,\"voter-age population\")\n",
    "print(\"state pop=\",np.sum(tractPop), \"VAP pct Hispanic is \",np.sum(tractHisp)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract Voter Age Population\", ylabel=\"tract Hispanic pop\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractHisp, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "237df16d-af87-47ba-acb1-2ef5702c1406",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is voter-age Black population by total VAP FL\n",
      "total state pop= 21538187 , VAP pct Black is  0.1395117153977754\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Black pop to total pop?\n",
    "print(\"this is voter-age Black population by total VAP \"+STATE)\n",
    "print(\"total state pop=\",np.sum(tractPop), \", VAP pct Black is \",np.sum(tractBlack)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract VAP\", ylabel=\"tract Black VAP\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractBlack, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "10b208af-8eb0-45fa-9dc0-8873eb797200",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of Census tract population for FL\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for curiosity, let's plot the distribution of populations among tracts\n",
    "n_bins=50\n",
    "print(\"this is a histogram of Census tract population for \"+STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractPop, bins=n_bins)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4aac60d6-4cce-469b-b075-ad743acda38c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is population by Census tract for FL\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of tract populations by area?\n",
    "print(\"this is population by Census tract for \"+STATE)  \n",
    "plt.plot(tractPop, tractArea, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f159bf55-a25e-45c0-b7ea-4c21fbce9bb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'shapely.geometry.polygon.Polygon'> <class 'shapely.geometry.polygon.Polygon'>\n"
     ]
    }
   ],
   "source": [
    "print( type(tractGeom[0]),type(tractGeom[1]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5e8f8f92-a336-43e5-8ac5-20c657cb5857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for nonPolygons in census tract data\n",
      "339 3336 0.00010229985600002488 nonPolygon tract no, pop, area\n",
      "2.804973950000393e-05\n",
      "7.425011650002095e-05\n",
      "2176 3930 0.05293367586549992 nonPolygon tract no, pop, area\n",
      "2.844272849998819e-05\n",
      "0.052905233136999935\n",
      "2574 5536 0.0002741033284999768 nonPolygon tract no, pop, area\n",
      "1.5471212499989262e-05\n",
      "0.0002586321159999875\n",
      "2575 6004 0.00032154478349998174 nonPolygon tract no, pop, area\n",
      "1.5495791500008177e-05\n",
      "0.00030604899199997355\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"looking for nonPolygons in census tract data\")\n",
    "notPoly = [0]*nTracts\n",
    "for m in range(nTracts):\n",
    "    if type(tractGeom[m]) != type(tractGeom[1]):  #assuming tract 1 is a single polygon\n",
    "        notPoly[m] = 1\n",
    "        print(m,tractPop[m], tractArea[m], \"nonPolygon tract no, pop, area\")\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        if y < 999 :  #let's zoom in on these\n",
    "            for geom in tractGeom[m].geoms :\n",
    "                xg,yg = geom.exterior.xy\n",
    "                print(geom.area)\n",
    "                plt.plot(xg,yg)\n",
    "            plt.text(x+0.0,y+0.01,m,fontsize=9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d34b5fe5-6f02-413a-8c7b-c6662fa756ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#convex-hull these right away  (decision for each state - YES for FL)\n",
    "for m in range(nTracts):\n",
    "    if notPoly[m] == 1:\n",
    "        for geom in tractGeom[m].geoms :\n",
    "            xg,yg = geom.exterior.xy\n",
    "            plt.plot(xg,yg)\n",
    "        tractGeom[m] = tractGeom[m].convex_hull\n",
    "        x,y = tractGeom[m].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "        notPoly[m] = 0\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a05c0ec2-d208-45b8-8ff6-c1f1f583ad5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[51, 99, 101, 127, 166, 174, 184, 186, 242, 243, 248, 264, 283, 318, 685, 750, 758, 1152, 1406, 1449, 1521, 1527, 1529, 1728, 1738, 1775, 2408, 2410, 2411, 2420, 2450, 2482, 2584, 2587, 2618, 2629, 2643, 2648, 2671, 2726, 2739, 2769, 2998, 3001, 3011, 3050, 3051, 3131, 3146, 3272, 3394, 3598, 3697, 3781, 3934, 3951, 4045, 4051, 4099, 4157, 4288, 4351, 4413, 4414, 4415, 4416, 4484, 4634, 4820, 4907, 4993, 5006, 5097, 5143]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For potential later issues, identify and flag lakeshore low-population tracts.\n",
    "lakeTracts = [-99999]\n",
    "minTractPop = 5\n",
    "for m in range(nTracts):\n",
    "    if tractPop[m] < minTractPop:\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        x2,y2 = tractGeom[m].exterior.xy  #turn these two on to show state map\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "        if 0 == 0 :  # y <38.8:  #only eliminate lake tracts south of Chesapeake Bay Bridge from map\n",
    "            plt.text(x, y,m, fontsize=9)\n",
    "            # isSkippedTract[m] = 1   #not yet\n",
    "            if lakeTracts == [-99999]:\n",
    "                lakeTracts = [m]\n",
    "            else:\n",
    "                lakeTracts.append(m)\n",
    "\n",
    "print(lakeTracts)  # not yet assigned as skipped tracts as well\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c72638c3-893e-4019-8ffb-51ccaa3e5684",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[51, 101, 127, 166, 174, 184, 186, 242, 248, 283, 318, 1406, 1449, 1527, 1529, 1775, 2450, 2482, 2618, 2671, 2726, 2998, 3394, 3598, 4099, 4413, 4415, 4484, 4634, 4993, 5006]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SPECIAL FOR FLORIDA - only remove the Gulfshore tracts from map - east coast are thinner\n",
    "#let's zoom in on these\n",
    "# For potential later issues, identify and flag lakeshore low-population tracts.\n",
    "gulfTracts = [-99999]\n",
    "minTractPop = 5\n",
    "for m in range(nTracts):\n",
    "    if tractPop[m] < minTractPop:\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        x2,y2 = tractGeom[m].exterior.xy  #turn these two on to show state map\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "        if (x + 0.3* (y-25) ) < -81  :  #only eliminate gulfCoast tracts from map\n",
    "            plt.text(x, y,m, fontsize=9)\n",
    "            # isSkippedTract[m] = 1   #not yet\n",
    "            if gulfTracts == [-99999]:\n",
    "                gulfTracts = [m]\n",
    "            else:\n",
    "                gulfTracts.append(m)\n",
    "\n",
    "print(gulfTracts)  # not yet assigned as skipped tracts as well\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "5f6b31c0-9741-45d0-b365-d69fdae3a98d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#zoom in on Tampa Bay\n",
    "minTractPop = 5\n",
    "for m in range(nTracts):\n",
    "    if tractPop[m] < minTractPop:\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        if (x+82.7)**2 + (y-27.6)**2 < 1 :\n",
    "            x2,y2 = tractGeom[m].exterior.xy  #turn these two on to show state map\n",
    "            plt.plot(x2,y2,c=\"purple\")\n",
    "            plt.text(x-0.04,y+0.03,m)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "da1f99a3-04d0-427b-95e3-d9a57836959d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "isSkippedTract = [0] * nTracts\n",
    "oceanTracts = gulfTracts  #now, assign these as skipped, except interior Tampa Bay and Everglade\n",
    "for t in oceanTracts:\n",
    "    x,y = tractGeom[t].exterior.xy\n",
    "    plt.plot(x,y)\n",
    "    if t == 1529 or t == 2998 or t==283 or t ==186 or t==2671 or t==248:\n",
    "        plt.text(tractGeom[t].centroid.x,tractGeom[t].centroid.y,t)  #these two appear interior\n",
    "    else:\n",
    "        isSkippedTract[t] = 1\n",
    "                \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "b0e9e246-a558-475c-8181-075a9421034e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5160 25\n"
     ]
    }
   ],
   "source": [
    "print(len(isSkippedTract),np.sum(isSkippedTract))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "d63968a2-afc1-4913-863c-4c415fb9c0b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for tracts with zero area\n"
     ]
    }
   ],
   "source": [
    "# For potential later issues, identify and visualize zero-Area tracts.  See Ohio code for more code\n",
    "isSkippedTract = [0]*nTracts\n",
    "print(\"looking for tracts with zero area\")\n",
    "for m in range(nTracts):\n",
    "    if(tractArea[m] == 0):       \n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        print( m,tractPop[m],\"(\",x,\",\",y,\")\" )\n",
    "        x2,y2 = tractGeom[m].exterior.xy\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "        #print(tractGeom[m])\n",
    "        plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "baabb633-83ac-4315-92bd-1d6fdd9e999b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now, manually flag tracts that should be excluded from the map, such as lakeshore empty tracts  #NOT RUN FOR FL\n",
    "isSkippedTract = [0]*nTracts\n",
    "#skipList = [1108,2676]   #Lake Erie and northeasternmost Susquehanna.  All others are small.\n",
    "for s in skipList:\n",
    "    isSkippedTract[s] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "72abde7f-085d-4c0a-abe3-29e8d6203b40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pct_std</th>\n",
       "      <th>county</th>\n",
       "      <th>precinct</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>G20PREODEL</th>\n",
       "      <th>G20PRESLAR</th>\n",
       "      <th>G20PREGHAW</th>\n",
       "      <th>G20PRECBLA</th>\n",
       "      <th>G20PREOWRI</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALA0001</td>\n",
       "      <td>ALA</td>\n",
       "      <td>01</td>\n",
       "      <td>725</td>\n",
       "      <td>424</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON Z ((-82.24245 29.85246 0.00000, -82.24...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALA0002</td>\n",
       "      <td>ALA</td>\n",
       "      <td>02</td>\n",
       "      <td>969</td>\n",
       "      <td>752</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>POLYGON Z ((-82.41775 29.92248 0.00000, -82.41...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALA0003</td>\n",
       "      <td>ALA</td>\n",
       "      <td>03</td>\n",
       "      <td>2036</td>\n",
       "      <td>1431</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>POLYGON Z ((-82.53335 29.84801 0.00000, -82.52...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ALA0004</td>\n",
       "      <td>ALA</td>\n",
       "      <td>04</td>\n",
       "      <td>1749</td>\n",
       "      <td>990</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>POLYGON Z ((-82.55700 29.65461 0.00000, -82.55...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALA0005</td>\n",
       "      <td>ALA</td>\n",
       "      <td>05</td>\n",
       "      <td>624</td>\n",
       "      <td>1789</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>POLYGON Z ((-82.34441 29.66672 0.00000, -82.34...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pct_std county precinct  G20PRERTRU  G20PREDBID  G20PRELJOR  G20PREODEL  \\\n",
       "0  ALA0001    ALA       01         725         424           6           2   \n",
       "1  ALA0002    ALA       02         969         752          18           0   \n",
       "2  ALA0003    ALA       03        2036        1431          32           1   \n",
       "3  ALA0004    ALA       04        1749         990          37           1   \n",
       "4  ALA0005    ALA       05         624        1789          36           0   \n",
       "\n",
       "   G20PRESLAR  G20PREGHAW  G20PRECBLA  G20PREOWRI  \\\n",
       "0           1           1           0           1   \n",
       "1           0           3           1           8   \n",
       "2           0           5           3          10   \n",
       "3           1           4           1           7   \n",
       "4           0          12           0          18   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON Z ((-82.24245 29.85246 0.00000, -82.24...  \n",
       "1  POLYGON Z ((-82.41775 29.92248 0.00000, -82.41...  \n",
       "2  POLYGON Z ((-82.53335 29.84801 0.00000, -82.52...  \n",
       "3  POLYGON Z ((-82.55700 29.65461 0.00000, -82.55...  \n",
       "4  POLYGON Z ((-82.34441 29.66672 0.00000, -82.34...  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, let's read in the voting data  \n",
    "VTDdbf = gpd.read_file(\"state_map_files/fl_vest_20.dbf\")\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4f3b9ddb-b971-472c-a353-521377f95e46",
   "metadata": {},
   "outputs": [],
   "source": [
    "#VTDdbf = VTDdbf.to_crs(tractPopFile.crs)  #MD's precinct file in wrong CRS\n",
    "#VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "3fe4ceba-8077-4485-ad0a-f8f2a5755040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6010 0.5169475466214156 10965776 = number of precincts, statewide GOP vote, total Trump+Biden votes\n"
     ]
    }
   ],
   "source": [
    "# Pull VTD geopandas data columns into arrays\n",
    "vtdGeom = VTDdbf['geometry'] \n",
    "# vtdGeom = VTDdbf['geometry'] #can't use VTDdbf because it uses alternate coordinate system\n",
    "vtdTrump = VTDdbf['G20PRERTRU']\n",
    "vtdBiden = VTDdbf['G20PREDBID']\n",
    "\n",
    "nPrecincts = len(vtdGeom)\n",
    "stateGOP = np.sum(vtdTrump)/(np.sum(vtdTrump) + np.sum(vtdBiden) ) \n",
    "print(nPrecincts, stateGOP, np.sum(vtdTrump) + np.sum(vtdBiden),\n",
    "      \"= number of precincts, statewide GOP vote, total Trump+Biden votes\" )\n",
    "vtdTrump = vtdTrump.to_numpy()  #these two lines try to avoid pandas complaints when we overwrite precinct data\n",
    "vtdBiden = vtdBiden.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c5c57f52-b8d2-4ef4-9d02-2429ed5e59da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ID the non-polygon PRECINCTS\n",
    "notPolyVTD = [0]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    if type(vtdGeom[p]) != type(vtdGeom[0]):\n",
    "        notPolyVTD[p] = 1\n",
    "        for geom in vtdGeom[p].geoms:    \n",
    "            xs, ys = geom.exterior.xy\n",
    "            plt.plot(xs,ys)\n",
    "        #plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,p)\n",
    "    else:\n",
    "        hi = \"hi\"\n",
    "        #x,y = vtdGeom[p].exterior.xy\n",
    "        #plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ea498c6a-d054-409e-92fb-cda921e2911f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#prep for geography triage\n",
    "#isSkippedTract = [0]*nTracts  #done above; already skipped two lakeshore tracts\n",
    "isSkippedPrecinct = [0]*nPrecincts  #will be used later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "3ded1b1f-5629-4d8f-826b-e037e5dcb3da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n",
      "working on tract 1600\n",
      "working on tract 1800\n",
      "working on tract 2000\n",
      "working on tract 2200\n",
      "working on tract 2400\n",
      "working on tract 2600\n",
      "working on tract 2800\n",
      "working on tract 3000\n",
      "working on tract 3200\n",
      "working on tract 3400\n",
      "working on tract 3600\n",
      "working on tract 3800\n",
      "working on tract 4000\n",
      "working on tract 4200\n",
      "working on tract 4400\n",
      "working on tract 4600\n",
      "working on tract 4800\n",
      "working on tract 5000\n",
      "tractMAP is a multiPoly!\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#build basic FL map\n",
    "#tractMAP = tractGeom[0] #uncomment for first time thru loop  **************\n",
    "for t in range(1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        uncomment = \"if-redone\"\n",
    "        #tractMAP = tractMAP.union(tractGeom[t])  #uncomment to build map  ********\n",
    "if tractMAP.type == tractGeom[0].type:\n",
    "    x,y = tractMAP.exterior.xy\n",
    "    plt.plot(x,y)\n",
    "else:\n",
    "    print(\"tractMAP is a multiPoly!\")\n",
    "    for geom in tractMAP.geoms:\n",
    "        x,y = geom.exterior.xy\n",
    "        plt.plot(x,y)\n",
    "pt1 = Point(-88,31.1)\n",
    "pt2 = Point(-88,29)\n",
    "pt3 = Point(-86,29)\n",
    "pt4 = Point(-86,31.1)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #clip western panhandle\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()\n",
    "wholeMAP = tractMAP  #in case we later slice parts out\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "e0995234-1df2-4531-954f-66fdc77e5155",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Florida has several improper geometry precincts.  Fix them now based on Jan'22 experience\n",
    "#OK, based on above, make 2561 its convex hull.  same for 2589.  2592 is squirrely also\n",
    "badPrecincts = [2561,2589,2592,2583, 5661,1733,3343,3283,1732,3280,1729,1737,1736,\n",
    "                163,404,188,5661,2561,2589,2592,1733, 167, 432,2828,2777,2830,2802]\n",
    "for p in badPrecincts:\n",
    "    vtdGeom[p] = vtdGeom[p].convex_hull\n",
    "    notPolyVTD[p] = 0  #this is no longer a MultiPolygon\n",
    "#from below, there is a problem geometry near -86.9, 30.39\n",
    "#let's plot the areas around some of these, for fun\n",
    "targetX = -86.9\n",
    "targetY = 30.39\n",
    "rMax = 0.1\n",
    "for m in range(nPrecincts):\n",
    "    tractX = vtdGeom[m].centroid.x\n",
    "    tractY = vtdGeom[m].centroid.y\n",
    "    r = ( (tractX-targetX)**2 + (tractY-targetY)**2) **0.5\n",
    "    if(r < rMax) :\n",
    "        if notPolyVTD[m] == 0 :\n",
    "            plt.text(tractX,tractY,m,fontsize=9)\n",
    "            x,y = vtdGeom[m].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "        else :\n",
    "            for geom in vtdGeom[m].geoms:\n",
    "                plt.text(geom.centroid.x,geom.centroid.y,m,fontsize=9)\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ae937095-879a-464c-9e7e-aa22cca2f41f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28 21538187 769220.9642857143 FL\n"
     ]
    }
   ],
   "source": [
    "#OK, hopefully all of FL's Frankenstein precincts have been smoothed.\n",
    "nDistricts = 28\n",
    "totalStatePop = np.sum(tractPop)\n",
    "avgDistrictPop = totalStatePop / nDistricts\n",
    "print(nDistricts,totalStatePop, avgDistrictPop,STATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e9000e13-7f14-45ba-8bc9-c5c38572e5d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-86.14970000000001  = x\n",
      "-86.14940000000001  = x\n",
      "-86.14910000000002  = x\n",
      "-86.14880000000002  = x\n",
      "-86.14850000000003  = x\n",
      "final population, eastX are 769227.6502686815 -86.14850000000003\n"
     ]
    }
   ],
   "source": [
    "#Now, a la El Paso - we will simply cut out a panhandle district, and make a 37-district map\n",
    "#Let's first determine where a vertical cut will create the correct district size\n",
    "westPop = 0.\n",
    "dx = 0.0003\n",
    "westX = -88\n",
    "startX = -86.15\n",
    "northY = 31.1\n",
    "southY = 29\n",
    "pt1 = Point(westX,northY)\n",
    "pt2 = Point(westX,southY)\n",
    "x = startX\n",
    "while westPop < avgDistrictPop:\n",
    "    westPop = 0.\n",
    "    x = x + dx\n",
    "    print(x,\" = x\")\n",
    "    pt3 = Point(x,southY)\n",
    "    pt4 = Point(x,northY)\n",
    "    clipPoly = Polygon([pt1,pt2,pt3,pt4])\n",
    "    for t in range(nTracts):\n",
    "        if clipPoly.intersects(tractGeom[t]):\n",
    "            fraction = clipPoly.intersection(tractGeom[t]).area / tractGeom[t].area\n",
    "            westPop += fraction * tractPop[t]\n",
    "print(\"final population, eastX are\",westPop,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1db86b67-c525-4b65-80e1-626810ce86ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "#continue with el-Paso-style cut-out of panhandle\n",
    "#Now, grab the El Paso pop out of the map, diminish tract and precinct pop's by this amount\n",
    "elPasoTractList = [-99999]\n",
    "elPasoPrecinctList = [-99999]\n",
    "eastX = -86.1485\n",
    "#westX = -107  #these three were set in previous block\n",
    "#northY = 32.1\n",
    "#southY = 30\n",
    "pt1 = Point(westX,northY)\n",
    "pt2 = Point(westX,southY)\n",
    "pt3 = Point(eastX,southY)\n",
    "pt4 = Point(eastX,northY)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])\n",
    "elPasoPop = 0.\n",
    "elPasoHisp = 0.\n",
    "elPasoBlack = 0.\n",
    "elPasoTrump = 0.\n",
    "elPasoBiden = 0.\n",
    "for t in range(nTracts):\n",
    "    if clipPoly.intersects(tractGeom[t]):\n",
    "        if elPasoTractList == [-99999]:\n",
    "            elPasoTractList = [t]\n",
    "        else:\n",
    "            elPasoTractList.append(t)\n",
    "        fraction = clipPoly.intersection(tractGeom[t]).area / tractGeom[t].area\n",
    "        elPasoPop += fraction*tractPop[t]\n",
    "        elPasoHisp += fraction*tractHisp[t]\n",
    "        elPasoBlack += fraction*tractBlack[t]      \n",
    "        if fraction > 0.999 :  #completely erase from map\n",
    "            isSkippedTract[t] = 1\n",
    "            tractPop[t] = 0.\n",
    "            tractHisp[t] = 0.\n",
    "            tractBlack[t] = 0.\n",
    "        else:  #keep remnant on map\n",
    "            tractPop[t] = (1.-fraction)*tractPop[t]\n",
    "            tractHisp[t] = (1.-fraction)*tractHisp[t]\n",
    "            tractBlack[t] = (1.-fraction)*tractBlack[t]\n",
    "            \n",
    "for p in range(nPrecincts):\n",
    "    if clipPoly.intersects(vtdGeom[p]):\n",
    "        if elPasoPrecinctList == [-99999]:\n",
    "            elPasoPrecinctList = [p]\n",
    "        else:\n",
    "            elPasoPrecinctList.append(p)\n",
    "        fraction = clipPoly.intersection(vtdGeom[p]).area / vtdGeom[p].area\n",
    "        elPasoTrump += fraction*vtdTrump[p]\n",
    "        elPasoBiden += fraction*vtdBiden[p]     \n",
    "        if fraction > 0.999 :  #completely erase from map\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            vtdTrump[p] = 0.\n",
    "            vtdBiden[p] = 0.\n",
    "        else:  #keep remnant on map\n",
    "            vtdTrump[p] = (1.-fraction)*vtdTrump[p]\n",
    "            vtdBiden[p] = (1.-fraction)*vtdBiden[p]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "91ecb793-846c-4761-8de8-d45506613189",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "panhandle pct GOP, total Pop are 0.6640657035858458 769227.650268688\n",
      "panhandle VAP Hisp, VAP Black are 0.0525490344939148 0.09723232869662814\n",
      "panhandle and remnant FL pops are 769227.650268688 20768925 21538152.65026869\n",
      "vs. census 21,538,187\n"
     ]
    }
   ],
   "source": [
    "print(\"panhandle pct GOP, total Pop are\",elPasoTrump/(elPasoBiden+elPasoTrump),elPasoPop)\n",
    "print(\"panhandle VAP Hisp, VAP Black are\",elPasoHisp/elPasoPop,elPasoBlack/elPasoPop)\n",
    "print(\"panhandle and remnant FL pops are\",elPasoPop, np.sum(tractPop),elPasoPop+np.sum(tractPop) )\n",
    "print(\"vs. census 21,538,187\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "fe47f683-719d-4a02-9648-905bddbf28a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tractMAP is a multiPoly!\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, let's slice out Key West, Everglades in a conventional clipPoly\n",
    "if tractMAP.type == tractGeom[0].type:\n",
    "    x,y = tractMAP.exterior.xy\n",
    "    plt.plot(x,y)\n",
    "else:\n",
    "    print(\"tractMAP is a multiPoly!\")\n",
    "    for geom in tractMAP.geoms:\n",
    "        x,y = geom.exterior.xy\n",
    "        plt.plot(x,y)\n",
    "\n",
    "pt1 = Point(-83.5,25.6)\n",
    "pt2 = Point(-83.5,24)\n",
    "pt3 = Point(-80,24)\n",
    "pt4 = Point(-80,25.6)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #LEFTOVER FROM INDIANA. plan to clip off SW corner near Evansville\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "cac3531a-35c7-438d-b5b9-5fcdf0745329",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  102 tracts w pop 401399.0  to reassign to tracts...\n",
      "[77, 339, 669, 683, 685, 763, 772, 773, 2620, 3094, 3632, 3808, 3809, 3814, 3855, 3856, 3857, 3858, 3859, 3861, 3864, 3869, 3979, 3993, 3998, 4017, 4058, 4059, 4231, 4444]\n",
      "I have flagged  128 precincts to reassign to precincts...\n",
      "[4065, 4432, 4433, 4441, 4442, 4443, 4444, 4457, 4478, 4517, 4528, 4529, 4531, 4532, 4533, 4534, 4535, 4543, 4551, 4557, 4560, 4563, 4564, 4565, 4568, 4573, 4574, 4575, 4576, 4578, 4579, 4580, 4581, 4582, 4583, 4584, 4585, 4586, 4589, 4590, 4608, 4611, 4613, 4621, 4627, 4628, 4630, 4697]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1  #uncomment after checking\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.05:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1  #uncomment later\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.05 :\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "d7009c9a-d5ff-4570-855d-1fb439afc32c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "0218d8e6-7832-479f-b44f-b37c117ee40c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "5d698e51-7b2d-40b8-b932-d22f20f09bb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "c17c1582-29d1-41bc-8f56-b32ac70747ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  401399.0 people (VAP of 307389.0 ) and  166221.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "55419803-eaaf-4181-8848-d8fdad0205b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, repeat for southern central tip\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-76.2,37.8)\n",
    "pt2 = Point(-76.4,38.6)\n",
    "pt3 = Point(-77.4,38.62)\n",
    "pt4 = Point(-78, 37.8)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #LEFTOVER FROM INDIANA. plan to clip off SW corner near Evansville\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2a56b3e5-803d-425d-8bb6-abfba9aa9716",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  142 tracts w pop 263227.0  to reassign to tracts...\n",
      "[19, 20, 22, 23, 29, 30, 344, 345, 458, 648, 651, 652, 688, 689, 690, 709, 710, 712, 713, 715, 717, 718, 732, 764, 767, 885, 887, 941, 942, 952, 964, 965, 986, 990, 992, 993, 994, 996, 1000, 1446, 1447, 1448, 1495, 1496, 2093, 2156, 2777, 2779, 2872, 2873, 2874, 2895, 3144, 3246, 3287, 3288, 3438, 3441, 4029]\n",
      "I have flagged  75 precincts to reassign to precincts...\n",
      "[297, 300, 340, 347, 348, 349, 368, 433, 436, 481, 526, 546, 757, 759, 760, 762, 773, 775, 776, 777, 778, 783, 784, 786, 788, 1669, 1671, 1672, 1676, 1677, 1678, 1680, 1689, 1700]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1 and tractGeom[t].centroid.x < -76.5:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 and vtdGeom[p].centroid.x < -76.5:\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "264c0a29-0366-433f-ae45-63b986566b34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "34f94200-4cc7-4226-9a6c-d6ae75b3380f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Tqrj0Joeb2j2+t3C0+igXfHUB1y+6Ho9s/85r6eGlvLP7HS5Nv5ShUUO7YIW+cf2g67m438UAOD3Oph2yverI+FE+zVe7fwkeBCmDZ3V6bXOjlAcZTcDI4Vd8EhxSSrdX3ZQIjBNCDAHuBB6QUiYBDwDNPGooCCF0wAXAZw2aXwfSUNRfx4AX6rs3t4Rm1vSmlHKMlHJMVFSUL5cRoItw2t1YqxwMn5GEyocqbbtW5OFyehgxo2OqlQ/Xn9i0JoR1rV6/q/g+53uu/e5aSm2l7Cvbx7aibe2e45P9n5AQlMAfJ/zR/wtsB1GmKB6f+DhB2iB2Fu9s2mHPfAiKgZghPs0XXLCVnKC+mC0xnV6bSgiMKhXWQPU/v9IuBaKUsgJYjmJ7uAH40nvoM1pXJ80Gtkgpj5cak1IWegWSB/hPg/FHgYZBAIm0rQYL0INkbVFyCkUkBLXRE1xONzuXH6XPkAjC483tPpfT7WF+A7tGYtipt+M4UH6Ah39+mHhzPP89RwmM21K0pV1z5Fbnsv7Yes5PO79duam6CiEEWpWWMEMYLo8Ll8fFtqJtvLf7PQ66a5Buh6Ku8gGjrZxqr7uvPxhrMbGirLp5+0uADtHmJ04IEQU4pZQVQggjcDbwLMrN/EwUQTIdONDKNFdzkppKCBEnpawvPXYxsMv793zgIyHEi0A8kA5s8PWCAnQv1ioHK+dlEt0nmOSMtj1XCg5VUVftJGNy2yqt5tiQXUaVzUVsiAGrw4XF2DWxC12FR3p4dsOzBGmDeP3s1wk1hJIRkcHnmZ+TFJzEyOiRRJuiW53D7rbz8M8PY9AYuDT90m5aeduU28uZnzWf+VnzG7WPtdn4X125Ej0+8d425xFqLS6P/2J8ZkeF8mjmUTKtdgaYDX6b95eMLzuOOGCZEGIHsBHFxrEQuBV4QQixHXgKuA1ACBEvhPiufrAQwgTM5MTupJ7nhBA7vfNOQ1F3IaXcDXwK7AG+B+6WUgYixXohbpeHb1/djtPhYcaNGWh0bTu/mS1KkJizgwkPl+0rQqdRkRJp6vrdhscDC38L+771y3R7S/fyq0W/Yn3Ben4z6jeEGkIBeHD0gxRZi3hoxUOtp+5AcUN+bM1j7CzZydNTnj5e/rU3kBKScvzvSQmTeGjMQ/x0+U9sNBrI1Grh0HLfJtIHo7dX+m2HcG6E4sOztLSqjZ4BfKXNHYeUcgcwspn2VSgutie35wNzGry2Ak3yIkspr2/lnH8D/tbW2gL0LNuX5lJ0uJrxF/YlPM43tVNIpBEhoKKoY2nWV2eVMjo5jDqnm4igFiKV/cXeb2DTW8pPn8mQOBoSxkDiGAhp347px8M/8uCKBwnVh/LkpCe5MO3C48fGxY3jp8t/YlvxNh5c/iCXL7ic8/qex+X9LycppHHqlnd2v8PCQwu5Z8Q9zEhuJgq7B3l43MPc+eOdAPxr+r8aqdCsKqEEAPqAMySJhKKtVLrchGo7r4arj+WI0fW8Su90IRA5HqBDeNweNn2XQ0xqCGNmp/g8Tq1RERRuoLK4/YJjT34Ve49VcdaAKEJNWsqtXZjvymGFJX8GSzKccQ+46mDta/Dp9fDiIHjMovwc3ezTdB/v+5h4czwLLl7ARf0uQpzk5RNqCOWspLN4Y+YbDAwfyPt73ueCry9gU8EmQNlpzM+az0ubX+KcPudw27CmaTp6mvGx44//XetUyr3uK9tHmiUNIdTgo+eYCE8hzFVNfkWRX9b1RUE5JrWKWVG+uYoHaJuACA7QIYpza3Da3T65356MJcrYIcHxr2UHCNZruGpcMkfKrGzPrWj3HD6z6kWoOAI3fgspk5U2lx0KdsLRTbDkT+B2wP7vlJ1IG2SWZzIjeQYhupBW+42NHcvY2LEU1BZww6Ib+NPqPzE7dTZLDi8hpyqHEVEj+OvkvzYRPL0Bh+eEILfoLeRW5XL5AqU+iF6jpxnnyGYxRihp1CuKD0FUx2xh9dg9HhYUVzAtPBizOhBH7C8CguMXRMHBTNZ//SmOujpMllASBg5mxDlz2h54Em6Xh/XzDwG+B/w1JDIpmB1Lc7FWOTCF+KZuyiys5rudBdw7vR8Wo5b4UCPlVidWhwuTv1UQxfth9csw7MoTQgOUGtiJXlXV2F/Dk5HH62K3hUVvOf4U7gux5lienPQkd/x4B2/teosxMWP49dBfMzd1Llp173QIMGvN3Dr0VkZGK5pt2UBQuN1OaCmX1UmERiuCw1p6CJjceuc2qHC6qXS5+TFg3/ArAcHxC6G8IJ95TzyKzmDEEhNLzo6tZK5b3W7BUV5Qy9cvbcVa6aDvyChCY9pvoE4ZEsG2JUcoPVqDyQdPLID31uZg0Kq4eZKSDynRG7+RX1FHv+jO1aRuhJSw6GElm+s5f225n1qrFB/yMeo72hTNvrJ9uD1un4stjYsbx9LLlwIQZghro3fv4L5R9+F0O3F6nESZTsRXmYPjoa7CpznCIpX32F3eSnEoHwnTKv/rEE1gt+FPAjaOXwhbvvsG6fFw3dP/4Jonn2f07AvwuF24Xb751teze1U+1koHEQlBTLtuYIdUJkbvLqOu1jcbhcvtYdHOAmYMiiHMrIxNCFUEx9FyP9cyL8mEQ8tgym8hqHW3WITwWW9/Wfpl5FTlsOJo+5I6hhnCThmhAUrk+KgPRjHq/VEIBCuuVK7XZY6C6gKf5hDGMKo1QWgrW6jv0Q7qvIF/k0LbjjEK4DsBwfELwO1ysm/1z/QbM4HgCCWwSmtQ/Nmddnu75lJ5Cxxc+MAIDOaOqUz0JmWja6/1zSV3TVYppbUOzhsad7wtPToYrVqw6oCfcxAdWKL8HnRB230tiVDSWvjSCWamzCTcEM6Sw0s6sbjej9114vP05LonqbIrKqIsdy3UFoHbt/e81BSPyZv11x+MDDn1AkV7MwHB8Qvg0JaN2GqqGXzmCfdNrb5ecLQvwV5pXg3h8WaMnXCFrRc4dqtvu51PN+ViMWqZNvDEDsBi0jJjYAxfb8vzb0Twrs8hZiiEt5AivCEpkyFnlU9uplqVliGRQ9hdutsPi+y9HKk+oV6anzWfbcXbANhkzVN2Z7W+5ZWrCY4nvLbzCSN6oxPB6UBAcPwCyNq8AYM5iD7DToTjaPSKUddp823HIaXk54/3c2R3mU+pRVpDrVGhNaix1bT99Fle62Dx7kIuHpmAQdtYTz0syUJJjQO7y095iKryIX8bDJzrW//UM8FWoXha+cDomNFkV2ZTUnf6ZmqttFc2el1fB72o/k5T45u6yhGSRFzdMWwu/8T+BrKN+JeA4PgFkLt7J4kZQ1A1cEfU1gsOH3ccRYer2blCUR2MODupjd5tozdpqK1qW2h9vS0Ph9vDFWOantPjrbGg8tdT5Zb3AAnDr/Stf+pUQMDmt33qPj5OiXM4ndVVqZZUEoJOJLO+6OuLANDW37nVvnmhidA+BLutFFR2LvN1/ScjIDf8S0BwnOZUlRRRVVxI0uBhjdrrVVUuH20c239UVBAXPziK6D6txyL4QuKAMA7vLMXRSuoRKSXzNuYyNMFCRnzTc9bY3ejUKnSaTn6MC3fDd7+DVS9B/9kQ3te3ccGxcMbdsPkd2PNNm90zwjMYFT2KN7a/QY2jps3+pyKx5ljuH33/8dfDo4crv+0OpFoHYa1Xh6zHEK70KyvO7tR6AoqqriEgOE5zinKUL15sWnqj9vbaOGxWF5FJQcSnh/plXRmT4nHa3Rzc1HJ08K68KvYVVHPF2OZ3ODV2J2Z9J90sPW6Ydx1seBOiB8F5L7Vv/Iy/KLWzv7kHyg612lUIwe/G/o4yWxmvbnu1E4vuvazJW8PDPz9MYlAiy69YzivTX2HL1eu42qlBpJ4JOt9S01i8LrnWspxOrSfPrtjRypwdy40WoHkCguM0p+SwIjgikxo/6Z3wqmpbcNSU2zm6r5yS3JpWdwjtITbNQkRiEJsW5eByNK/HnrfpCHqNiguGNx89bHW42xf8V1sCCx84kS6kLFtJYFh2CC57G25bDiFxbU7TCI0OLn8HPC5FeLTBkMghXDXgKj7Y+8HxdCKnAw63g5+O/MRDPz9EjCmGeefPI8KopKjTbv0QTV05jL7R5/mCIlIAUFW0VTC0dXReNWaCoYvzmv3CCAiO05ySo0cIiYpBZ2zsjqjR1ds42lZVrfw0E+mRmC061J1VC3kRQjDp0n5Ul9o4tK2pHtvmdPPNtnzmDI1rMXW63eVBr23Hej6/CTb978Trf46AT38FocmQcWGLw9okJAEMoUpKEh94cMyDhBvCeWbDM6eNymrqvKn8ZtlvqHZU8x9tCiHvXKDk+3LWwYpnFHuQr04HQHCQInSkrbKNnq0T5X2wqPaTkT2AQkBwnOZUlxRjiW5aSU1rqPeqanvHoVYrT21X/GGc3wQHQMKAMHQGNfkHm94cvtqaR7XN1axRvB6704Pe14hgj0dxne13Njy4X/GIAkDClIfAx2juZtn9FVTnw6T7fOpu0Bj42+S/cbDiII+tfey0KDDUMJ1Kn93fQf5W+Hs/2PoBWEth6u+UgEkfsUvwINB20kgRpFETpdOQXde+eKUArRMQHKc5tRVlmEObRh4fN4472v5CxaYp+aj2rT3WRs/2oVIJYtMsHDtY0ah977Eqnly4h9F9whif2nJKEofbg74tQXZ0Myx6BJ4IU+IIBp2vGLVvmA+/2QHXfg6jb+j4RXg8sPJFiBwAA8/3edjkhMncNeIufsj5gec3Pd+hmuO9ibl9ld3EjOQZMOZGpdFZC989pDgbpExp13xVTicqJDo/JCascLopcQRsHP4kIDhOc+x1dehNTQ2Sx43jLew4jtocLCutQkrJgAmK3v/gZv+kuW5IXL9QyvJrqS5T1lFe6+C29zcRbNDw+rWjjkeqN4fd6W4iOKxWK7b6azqwBP47Hda/rrzWGGDYVSc6h/WB9Jmdu4D8rVC0G8bfDqr2fZ1uGXoL1wy8hvf2vMcfV/0Rp4+lVXsjGeEZAMQHxSs5vuIUbypih8K5T7VrtwFQXVsBgMrQuVTodW4PTimJ1vXOxJCnKoEkh6c5zjorOqOxSbtao0Gl1rRoHB+zdg8AuWcOP15TfOQ5yX5fX/9xMWxckM32H3OZfEU6f1+8n8JKO/Nun0B0SOtlPu0uDyFe+0dNTQ2vvPIKdq/NJiZIw7Can5gEMPdFiBoIKZP8vn7CU0Frhtz1SsbcdqASKh4Z9wjhhnD+te1flNnKuGbQNWREZBDpx5rb3cH1GdfzwuYXcHm8T/axQ6HyKNy+st1CA8BaWw6Axhjql/X1MQaM4/4kIDhOY9wuJ26XC52hqeAAJQiwLeP4lqpaag9VYgjS0m90G0n/OkBIhJG00dHsXpPPqAtSWLAtn/OGxTEyue3EfnaXB71asGLFCpYtW9boWGGNiz2kM2nmRe2+obcLUzhkXAAHl3ZouBCC24ffTrgxnKfWPcXq/NUADAgbwMSEidw4+EbCDb5lEO5JhBAMiRxCZnmm0hA3QrFvVB9rd7VEgDqrIjh0ps4leKzfsJ76VqTeRUBwnMY46pTMsc3tOKBlwXHUdiJr7Y+lVUxWKyqYrsr7o04y4dro5rKnVlDtcHHp6ESfxtldbgzCybJly7BYLEyfPp1hw4YhhODj539HRZ3ZZ4N1p4hMh+0fg6PW5ziFk7m8/+VMip9Efk0+24q3sTZ/Le/vfp/Veav537n/w6Lv/dXroo3RZFVmKS/qgygPr4Ghl7V7LodXcOhNoZ1a0/HI8YDk8CsBwXEaUy84tC0JDoOhWVXVf44WoxYQp9eysryGs/RqnPauc2cs86ZXL6pzEBOm54y+TUrUN+IfH8yn4uAWwlyxBGvNCCG48soriY8/8WSrVQucsptMeKHeGJnywxCT0eFp4oPiiQ+KZ0zsGG4Zegtr8tdwz9J7uO+n+3jznDfR+5iuo6foG9qXZbnLqHPVYUwapzRWHu3QXA5v7Q5TZ3ccXtHhCew5/ErAOH4a47B5dxyG5lNKa/RNBUeVy82H+aVcEBXK7EgLB6w2dCY1bqcHl7NrhEeF1zDeNymE/zt3YKsGcZfLTcXBLQAM1RSgK8uiX79+jYQGgEYtcHWX4AjzZtJtI3K8vUyMn8jfJv+NLUVbuH/Z/fx89Gfs7t7rVpoQlIBbupUkjoeWK41hKR2ay12nuGibzZ1T04mAqqpLaPObJYQwCCE2CCG2CyF2CyEe97aPEEKsE0JsE0JsEkKMa2bsAO/x+p8qIcT93mN/F0LsE0LsEEJ8JYQI9banCCHqGox5w7+X/MuhbVWVAddJguPD/FJq3B7uSI6mj1FPrduDzaD2ztc1gqOmwo4LyUd3T2xTTfXFT+sAKFWHowtTvL0iI5sakrVqdfftOGIyQKWFoxv8PvXs1Nk8MPoBVuWt4u6ld3PHkjt6bdyHw63sHPeX7YfM70Gta1fQX0M83sA/g6lzKrr6T0B9QacA/sEXVZUdmC6lrBFCaIFVQohFwBPA41LKRUKIOcBzwFkNB0op9wMjAIQQaiAP+Mp7eAnwqJTSJYR4FngUeNh7LEtKOaIzF3Y6kr1tM1HJKQSFN1XlVJUUk7d3F+njJ6HRKR4kTu+Ooz69yMlo9XocVuvx11JK3skrYYLFzPBgE4XePD8/rsolGlCpu8bGUVVoxagVaNRt3+h3rF+FFpgzczrnjssgKyuL1NSmtTO0Wi0uXEqcRTvdZNuN1ggJo+Dw2i6Z/qbBNxFljGJ78Xbm7Z/HyryVjIsdh06tQ+VjHe/uoN6IX1pXCqYIJW5G1TFtuKiPGNd3LqGmEIJxFjPfFlfy+75xgfocfqLNd1Uqjzf1eRG03h/p/al/Vy1AW1VXZqAIhMPeeRc3OLYOaL8F7RdE8ZEcvnz6LwCkj5vI8JlzSB4yDKFSsX/tShb+41kAjO/+B53JhKOuDmOw8vZ4XM0HP13m/Kfybtb9CYyhZNc5OGxzcE8fxXsqyZvfp1glueiSfh2u+NcapeU2LFVu6OubUVnlsoGAc8dloFKpSE9Pb7afxhSCk2o8NUWoQmL9ueTmST4D1v5LSbOh82+1OSEE56edz6SESczbP49HVz6KzWWjb2hfzkw8k8v6X0asuRuusQ00XiFRbi9XKiOG9umQKy6AsFdhVZswqTtvhr0+PoJ79x5hdUUNk8P8WJ/+F4xP74p3t7AZ6Ae8KqVc71U5/SCEeB5lRzixjWmuAj5u4djNwLwGr1OFEFuBKuCPUsqVzazpNuA2gORk/8cX9DbqqpQnsJi+6eTu3cWBDWswWUKxVlY06hc/YBBavQHp8bB/rfJvU7WVTmPP1zD6RvbUKDuUoUHKjc/mrXcRZtF3SQwHwJJlOWgRDBrftsvmtoNHUSFRx6ajamMXYbZEANVYC7II6g7B0WcirP4HHFgMgy/qklMEa4NJCk6iyFrEwIiB7Cjewb6yfSQGJ3JRv645Z3sI0ioFviqsxZD9Mwy9vMNzaexV1GqD8IcIPi8qlD8fyOO9/NKA4PATPgkOKaUbGOG1Q3wlhBiCctN+QEr5hRDiCuAt4OzmxgshdMAFKOqok4/9AXABH3qbjgHJUspSIcRo4GshxGApZdVJa3oTeBNgzJgxvVPp61eUJ7ep195EfP+BZK5bxaJXX2zSa/CZM0gfp8jwc+/8DVXFxYTHJzTpB5BvHE583XbY+TmMvpG9tXWogP5mRbVV6k1FravquojmnG0laFWSKRObX2M9Ukr+/eEXxAi45Jypbc5riYoHcqgsyCaofxcE/p1M0ngl0eH8eyF+RIeNwq2hVWtZcNECJBKNSsP8rPn8YdUfGBY5rO3B3YBbKjYwldsFjhoI7XjBL62jGpvWPzd5o1rFFbHhvJVXTLHDSZQfo8illEhkr1IZdgftulopZQWwHJgF3AB86T30GdDEON6A2cAWKWVhw0YhxA3AecC1XpUYUkq7lLLU+/dmIAvo3551no7YaxVtoSEoCCk9rP38Y0KimiYvLM09UfNZqzeQW2Xnp3U7mp1T1H/Yc1ZBZR57a2ykGvWYvLaGwlrFgye+jQjujlJnc6IvtmOPMaDVtrwr+t2L7/D4448TIxXf/sF9274hWWKVOIKK4s7XrfYJYyj8erFS32Pxn7rsNGqV+rhKqMquPEuFGTrnsuovsiuVFP4RIQmKYby2tMNzGZ3V2HT+2x1cnxCBS8Inx8r8Ml+5rZxKeyXPbXyOKxZcgdVpbXvQaUSbOw4hRBTglFJWCCGMKLuKZ1FsGmeiCJLpwIFWprmak9RUQohZKMbwM6WU1gbtUUCZlNIthOgLpAP+9XM8BbHVCw5zENLjoaKw+YSDqz/9gOHnzsUYpHzp7vvfSo5ootk+fCAWc2MBIBv+tftL9qpmMDjohAdWkVt5gow2+N+2cSSvmo9e3UqwFKSNb1mVVFFTh7kq5/jrG2+726f5Q2OUHUxFuX9uFD4RNUCpObHxP0r98g5ETLcHs1axC1XaK3tceJTbynl6w9MATHapwe2AxDEdns/orMFhjvLX8uhnMnBGqJkP8ku5Ozm62XLDUkqEEFRX78HpLCc8vPFO1e22olIZEELFXT/exa7SXWiEBpd08fym5/njhD+iEiqklFQ7q3l6/dNc1O8ixsWOw+Vx4fA4cLiVH6fHicPjwOl2Km0eb5tbaYsNimVwxGC/Xb+/8UVVFQe867VzqIBPpZQLhRAVwMtCCA1gw2tvEELEA/+VUs7xvjYBM4HbT5r3X4AeWOL1dFgnpbwDmAo8IYRwAW7gDillN377eyf1gkOj1+N2uZh89Q2s+vhdAPqPn0Rs+gB+/kCpNVFVVHhccBzRKIbu4U8u5Y5+bh655YIGswqqXEZCkgfh3vUlOelTuCzmhN/8PquNSAcU7CzD6XCj1XU+UynA/oNlfPPiVgwe8AwN5bxzmnpF1fO/b34CoN/4s7lu9mSfz2E0GjEIJ+XV3fwkOOYm2PIuvHaGksdKpYVJv1HcUv3s0dMnRAk8PFpzlBRLil/nbi85DYR76v7FoDFC/3M7PF+Qs5pSXZofVnaCX8VHcueew3yQX0qwRk2Vy02UToNTSn67N5taj4ql8QsoyHvn+Jg+ybfjdls5mvf+8TarB3aVnrC+zOwzk88yP+PHwz8qjgENWHhoYYfWqlPpWHnVSkxa/zpa+AtfvKp2ACObaV8FjG6mPR+Y0+C1FWjiPyql7NfC+b4AvmhrXb806mMyXr/12kbtQ6adw7l3KGk16gWHJbrpE7zebeeT/Q4eOaldAKRNR7XqJQx9bWQEndiV2DwegvRqXHY3tRV2QqP98yH+/O3dmCRMuXMIo4c3VbfV43S5Kcjchkpt5NpZ7bdThOncVHRR7EmLRKbDdV/Clveg9KASOT3vWhjza5j9LKj9t3tbfnQ5ALGmnveoSgpW1IfRLhe6nCUw9pYOp18BMLtqKNT715A9J8pChFbD/2U2F82uqGdz8j6h4b78SO7/EEKNWh2E2608vB1xKH2vSp3M8MS5zEieQV9LX3Kqcii2FrOlaAtXDriSA+UHGBQxiCBtEDq1Dp1Kh1atRavSHn+tUys/WtWJ9qyKLP685s+sP7aeacnT/Po/8BeBlCOnCPHpA5ptD444Efw29oJLqauuwhCkeLdIKQmVVvoYHCSFGlhUYMRpt6PVN0xdISFhNEK6GVxzkEFBJ54R0owGfvL6JNhq/WMg/3H1EUJLncgMS6tCA+CDH9ZhEk6i+w/vkP99qElDUblWSVTUnf77yeOVHwC3Cxb/UUntrjMpKccdVtj8DhTsBLVG8UCK6AdXfayUovUBp9vJJ/s+YU7qHPqFNfsM1q38YdUfAPhN6HDIXQSjftWp+UxuG061f21repWKBaPSya6zo1cJzGo1aqGUlz1r437ONWQx94zduN12VCodIE/YARuwYdnVwC5uH/MEkSZFnXbPyLbLBvvKoPBBPL3haVbnrw4IjgCdI3XkGG7+x7+x1dTw84dvc3TvLgBstdXH+0y99qZGY1btOESFMHFlvBqbB9xCQ2mtHVthGX+Zt5brq9SMEholeA0YV7uf5Aa1mYM1KpyAW4CtpvOCw+X28POXWYSq4JYbh7bZPycnGwH8+pKOqTzCQoLILAdPXTkqUw9lmFVrYPYz4KqDNa8oQYLF+8FRDcZwpbRqUBQc/BGObYOk1nxMTrCrdBd1rjpm9ulkPRE/UZ8Gfo7b+/k5sBhih3R4vhpNEGpHddsd20lfk56+psY5vw5482mNCFba1cdzgjX/sLGmKJM+BsNxoeFvtGot4+PGsypv1XG7S28jIDhOIey1tcx77GHcbjcjzp3LGZddg97Usvro7SXb0XhU/GrOBP764TJUUktosIkbnp/Hek8852kMOFVaCI6lVB/B5LqsRkbDeu8qp0Zg98OOY+3GY8TUSoLGRRIS0jRh3+HCMl5+4y0MBgPpGUOh6CBWbQj6DrpPhoZH4j5soyb/ACH9xnd2+SAlhZsXsuanRRS7TNzyyHNtxpMcZ9azYI5WPNgGXwQjrlFiPwAqcuEfQyBvi8+CY/2x9QgEY2PHduxa/ExpXSkpISlo6ryZlYdc2qn5qvVhaGqb1qLvCjYVZwEWxoT75sygEwJ3F7vfTkmYwvLc5eRU5ZBqadkG2FMEBMcpgsNWx3f/eh6TJYyrHn+WkKjWa2PU1tlZXSwYrq8kISmeg9WCWFHDzm272OSK5uwESaqwIMoE+ZWlRDkqsIQ3zhNl9N4UHRqw1Xa+9GZJvqIjbqmux1uv/ZMQAdTVcnjzcoSAfgM77lkSFhMPHKXiWHanBIfLWkXO6i9Yu3EzWY5IIBoDbddqb4TWANP/0Pyx0CQlyjpnJUy4w6fpVuatZGD4wF6Rbn1V3ipW56/mooRpsOsTGHurUl2xE5SHpBBVvt9PK2ydvdWlgIXhkc2rg08mPSSexUXZON1OtH60WTVkUoJi01uVt6pXCo5fVtTKKcwPr/2DioICZt/9QJtCA+CDb1djFzqunqjov8tdavIJ4ZrPclAJePSKSSAEAknm7sVopZuojFmN5jB7dxw2gxprtaPJOdpLdY0yR7h3t+FwunB7XX6PFBQdL7ozesYFpI2fycxLruW2yzrumRMamwJAeUlh6x1bQHo87P723zz/3FN8sPowBc4gpg8Mw6Kykmhy+r7b8IXUKcpuxNO2MX9XyS52FO/g/DTfa5x3Jd8e+haAkWozuO3KtXQSe3g6CdY86my1nZ6rLbLqPERSToiPxvzBkUNxSthTuLrL1pQQlECqJZXVeV13js4Q2HGcAhRmZ5G5fjVnXHYNSYN9ixL+Yks+IR4dF81UbrxXDQ5mwd4KYsySB84fRVpcGJsQSEB/WElNUlRXR3IDQ/L40CBUwL5BJsbl1bRwJt85vLcMs5D0TQhm+Za9LP7mc3TCjV0bhNAHoQOsYf04f8qoTp8LIDTOGwRYUdmucdLlZMl//8K+IgdlniBitS7OGNOPjGlXIF0Olj33HCMi/PzMlXqmUjGvYKcSed4KK46uQC3UXNzvYv+uoYMMjhjMwkMLmRnmtWlYOh4xXo8pZQLqna9zYN9yho3oWIZdXznsNJKkqWq7o5cR8WfB7vlsPbac4fFnddm6JsVP4rPMz7C5bBg0XROE21ECguMUYMPXn6EzGhk154K2OwPbdx8k0x3KpUkuNBrlLX7oV3N46KR+AhBINAmjce//mDHfXINjgRbdI0dAZyLRoOOSmDDmu8uYvq6uU9dw+EglYeUu3ANC0GpULJ8/D513h6F31oCzBo+Ea+ac2anzNESrN6DHjtXWvviTQ8s/YE2BjhSDnTP6hTHqgodR65TAyCO7fkaiIj6pc6qYJiSfofzOXd+m4DhcdZhYcyxBuiD/rqGDVDmUm67ZW3yJ4LhOz9k/Ywa2b3XU7FkIXSg4pJTke8I4x9B8QG1z9AkbhgrJ7pLmMzL4g9yKUiL0ydjddubt/p4bhl/UZefqCAHB0cuprSgnc90qxl54GQazbzeKf8zfhAoz91x8RusdhUBIydipvyY7ZRyp/5uMzuPE7XFSf6v9TZ8YPi8o44cw2SSCsz2sWnkUgWDc9CTe/m4NAM6gWBJS0oiOCCUpJoKhaYmY9L65o/qKGomnHfUrpNvNsnXbCFEZuPa3T6PVNV5PfuY2AOKG+B6M6BOhSRCSAEfWwfiW/9Nuj5uNBRsZGd0ktKrHKKkrIVgbhOqH3yupRoI7H1diMgazIXEmQ7O+obLmKSxBXRMZX+usxYqJOB8+dweLN/LbZXdyuM6GB8GSgtaSZXQMj8fD9V89wfbqrxBCqSHy/LY/9TrBEbBx9GKkx8OmhUr5kgFn+KY3/nHtTpZXmJliqSE1sfU4iYaxDanJQ1k98n4A3A1KlKabDYyvVbEp3cCx8o7tOqSUHNlZQoVaMnZINJk7NwNwx/VXcNdlM7ls2ljGZ/T1u9AAUOHB3Y4UmAd//pSjrjCmDktpIjQA8vPzCRI2QuKbT+feKZLGKzuO1tZXcZCSuhLOSjrL/+fvIFuLtnKHx2sfSD7DbzEzEVPuIdhdy66f/umX+Zojz5tPK0bftpH7oWV3kV1nZ06c8t53RWjpogNb2FHzBZFiFHPj7sNtTcZWOLfXFe8KCI5ezP51q9i04EvSxownOqVvo2Pvfv0zlz/+EXszDx9vq6qu4eGv9hAs63jh9tk+nEF4rRxePIrL7caFfyWv4ODx5ocGJeDQCj7c1f6EgVJKXnh9MyEVboLSQ3jrm58w2ssJ7j+BpJiuj61QCYnH49uXTno8/Lx2IxZRy4g5NzXTQXK0GuKDu8ivPnkCVOVBaVaLXbQq5QZXZC3qmjW0E5vLRnZFFpfm7oXEsXD9V20P8pG09Ilsi53CkB1vUlLZNa65BV71WrS++SqZ9bg9bo7ZbcTp1Dx9zle8PuUP/GXk9X5fz+GKAgDuHHkzz5xzK5PNj2O2Tff7eTpLQHD0YkwhoQAMP3t2kyCgd9bnsrHOwty3tnPVkx/z/cpt3PrC15SqgnhsZgqREaFtn0A0FhyxI5RaWpO2v0LCG6PZsvEz5XVqOEaHh60dyPv03fIcDDsqsUZqqNQfJHf7Gqo1odx9ebMZ+P2OQPr8tHZ4/UJyHSFMGhSPRtfUGFlxaBtlMoTUpC5KXtj/XNCa4Zu7YfXLsPZVcDeOn0kN7sNETRgvb3mZT7/zzXW3KzlafZQMmw2zvQbG3QZt1X5pJxGzHifIZWXPt4/7dd56im1KkGGkofX0JlvylmD1wK/6K3bGyX2v4rJh/+f39RTVKmn5Ei1KQOWRMisVVic+Pvt0GwHB0Ysxh4YCYKtpHEHr8XgodBtI11QxPbyOzdVG7vg2j/W2MC6Jd3DJTN+CyJQdxwnSUkex59LPqFUrT1/ata8CoFKrSLHCPtofy2GrdACSWs1PeAr341ZpuOvGqzFou8e8pgKfBcfKn5djpo6R5/262eOHtq8CoO+wNmxHHSUsBeb8HY6shSV/hh9+D5/dCPu/h8wfwO1CHPiBfxzcwTi7i5cKV1JypGfdNZ0eJxNs3pgWU7iS3sWPJKWMZku/y5iY+REHcrb6de5SWwXP59ZioI4BbXiCLc3+BoFket/OBTa2RbFVERzJFiUqfUCsItB257fPM7CrCRjHezFleUoqhLD4xoF5B7LzsKoMTOtr4Pc3z+VYYSnzf95KWkIUZ08c3qlzZgw9B4YcY933zzJh/dPs27+GgQMmMkil5SuDi1qXi/I1+8hato8J952DPqz1mtD9UkI5ELIMAJdKx6P33Y05uPu8gYTAJ+N4/o5lZNWFMKOfHq2p+WvKPpyLGUF0un/chZtl5LVKVl1rGRTthWV/hX3eDKuxw8BehdGSxB/Pf4dLfriBa368gzdnvEpKn7aLW3UFEklNfTzLB96b6oS7YNbTfjvHgPP/St0/v6Vs/v/huecHn+NniuqqMKo1BJ9UyrewtphPcjbyXrGaAhnBaym1xLSRwn1t4Q5S9WriQru2aFaFvQIpBXHBijNAWpTyXUkO711ZcgOCoxfjsCnGaHHSF2X1DsX+MH6g8pQUFxPB7R1Q/Sjqr2ZuqkIwZOrtVG/6J5VrX4MBExkVauZLTxWrj1SQ+8EhbKpwtP/+kYmPXILH46Fg7V7iJmY0Uql5PB7Wvb4Ve4xigNS6bLz4j5fQOp2kScmoqWfS75yZ/g2kO/lSkEgf9vmrlnyLHi1jz2u+3of0eMiuEqRaPE3eD79Tn4pk4NwTf1flKzuQ2mK45D+kxo3mv0Pv5cZd/2LBppe5t4cER7Qpmo+Dg8gYdx8XlRbAhn/D5nf9KjhCLDFka6IYVb6Zd3dnctPQgc32k9LDztKDzM/P5scKD/vccQRRw2dDoxgZOQC7y8GLuxbw3/JYaoknTpTwZpqbucltu4CX2msYEhLe5XmjKu0VCI8ZjVpR+a3PLmVQXAihJv87jnSGgODoxaQMV55sP3jkN4y98DKmXH0DQgi2ZRejkkYmjOhcYUSPSoOmBd+QIHMYGxKn0S9vJW6Pm7MHRPHHvVUsO1RKH5QP8dacUHJufI9yg7IjOmv/UfqcM4ov/7KMaq03a6/QITwepAoSbDaSoqMpqa7mgNvNvnVrCVm2jFHJSZxx/fXoQ1rfvXQEjQpc7tb9X4ozN7Kn2syUJBWG0OafPIszN1EjjfRNiWz2eJcgBKQ0SCc/YBbUlUOoUv999Ojbid/xKvm1Bd23ppOIMESAEOTp9TDnOSWZ4+5vlAh4P9k7HLUVJNjzWGMZyStHS7hhsKfRw8bPx3bz70O72OkIp4goIIY0cYTrg/byZU0Sj+7Zx0fjYrh8w8/sdqcxSnuUv6SHMzZqus8PLaFaNbl1vgcJdpRaVyVqqXioOVweNh8u56qxyV1+3vYSEBy9GHNoGMYQC3VVlWz85nMsUdEMnzmHfaVOYoQbs6lz0aQOTQjBog6304Fa2/SJxtNnEhGHF/Lf779mX4WahOBk5ntquF2tI9KeT7ku/rjQANi0pgqPeudxoSGkm0GJtahj4sg5nM3Vf/oT2mBFZ1tXWcnWz79gc+Z+lhcXs/aZZ5icksKU227r1DWdjFkLNQ5Pq31Wf/85GjRMuKBlY/Oh7Yotoe/InnmyB0AfrPw0IE5jJr8Lssj6ihCCaFM0hyq8RTpTz1RqkRxaBv384wBRlbONSJxkBo8j3xDEutx8JvZRPnev7fmBJwuiCBLxjNBXcJOliAvi0+gber6ytp0LeKGkDxlrc1ATz9/iS7i5/9x27xwMKkGe3dml+akA6tzV6ITyHu84WoHN6WFC3ybljHqcgODo5Vz7txcBWPjys2z74VuGzpjFEaeRsZbO547SWmIhFwrzsolPaZrgTWtRvpwL7TWsizthOykJUXPTLVOpKatj7dsbmHTTaLKW72drdgSrV9lRA2MHOxhx52zU3tiMk2+3RouFib++mTOkJHPxYn5aupSleXnEL1tG2jT/1SAINuooKW/ZqF9xZA87ynSMjZGYo1o2kB7KzSdMJQnt03Y6+O4kQRfKekfPGk4nxE1gxdEVuD1u1PXZfat8j8RuCenxkPvDP/EcXEYkMCwlA4BFOUeZ2CeR7OpCni4MY4T2CB+OOZNwY9MgwbsHns0Lq5RkiS/3qeayvh0TZlenX8Rj2z/l0x1/59qRv+/wNbVElc1KiMGE3VNNiEaJv1qfrRjKx6X2UEmAVgh4VfVyLNExWKJjGDx1BiW5h1m9YjU2lZ6hiZ3PiqpPUqKPKw42H3RWXXYEgGJdGMnFJ9Qhg2QhwenJxI0fwCVvXE/M+AwyrpyM8Lhwq/VkhB1j9P0XHhcarSGEYMC553L5bbeDEGxZuarT19WQ4CAzNdKAx9l8Nts1334IwBnnXdfiHG6Xi8M1WvqGqbu3IJQPxBujKFarcHoD2XqCzPJMKu2VfLL/E6VULhyPCeoMh+c9TPL6v5BSuhyAWE8twU47G2vtALywfxMeKfjn4IxmhQaASWvk3X4O3u/n5LK+HX8guWjoIyTqNby991Nc7s4/tDXk8WXvMWneeMa/fTFOVRFmjfLdPlhUQ5zFQLi5d9k3ICA4Thn6jVNcQL9boqhMxg3qvN6zz6BxOKQa2+FNzR4PyVtHoS6cLGMS96oc/AcP1+4u4PzfNA1ICk2J4urfDmJAWBGj7vYl+PAE9poaPn7rv6g8HoZN8EPdjAZYQsPwoKbm2MEmx2oKD7OlUDAs3EFoUvMGV4D83auwoyO1b98W+/QUySF98AjBwdyVPbaGQeGDAHhp80tY8aoFnR3LMuBxu/F4PNSVHyNm//uNjoVkTGOQdJKp1lNgrWJ+dRQz9AdID2/d1ndu0jhmJnWubolareXmQVdT6HTz6fanOjXXyRwoV1KXWFUHESoXIbpQALKKa+gX3TvykZ1MQHCcIphDw1Brtewvc6KSbsYNU9IeFFXZyC2z4nC1rsdvDktIEIfUqRiLtzd7PLlgA7n6WBACj8HI+dNG8cI9swhKaN5AHDYwibOfvoqgPu1LcvfDP1+hVK9nbloaA845p93X0RqhkUrepPJjOU2OrV/wNi7UTJ59RatzHNq5AYDU0TP8ujZ/MHnoDWil5Jtd7/TYGp6Y9ATvzX4Pu9vOZ0eWgMHSavR7S3hcDkqfGYrqiTCMLw9Ej43iy74mZ9TvKTrvPcISB9DXno9VZ2DE+kM4pYb7+nZf2dxLhj5IvF7Lm3u/ZNWhT/k552t+v+RKzps3hjU5X3R43rzaw41eXztESZdfbnUQGdS04FlvIGDjOEUQQhAaE0exKwqDcPPbz3ayMaeM0lpl25wWZebvlw9nVHL7ksGVWYYwtHwx0uNGNPCCqXvrPiJthWwwpxNTWc7V5/o5qZ8XR20tu2qqSQVG33CD3+cPi+sD7KS8KJ+G+WxtFUVsOOogI9hBZProVufIzismVu3CHNvztb1PJiyiH2erw5hffZD7aosxtRGP0FWMjB5Jelg6b+9+h1+F9kFUHPF5rMtuJfc/15FaspSGqz8y6E5ShkwjasgJFdP1/eKpLfiSYvowMzaOMXFz/HgVraNWqfnD2N/x6NqnuHPlkwBokLiB7w7MY2KKb8GBNXYbZ354GU6q0HrCcWiziGA871/4LKFGE8He9CeVVicWY9cZ4jtDmzsOIYRBCLFBCLFdCLFbCPG4t32EEGKdEGKbEGKTEKJJuLIQYoD3eP1PlRDifu+xcCHEEiHEAe/vsAbjHhVCHBRC7BdCdLySzylGVfUu9u59lMKi75o9PvueBwnVq7Gi4/vdBZTWOrh0VCK/OqMPWcW1XPLaGl5b3lQl0xrqxJEEYyXv0N7jbXXLv0Cf8x7VtnBMQ+5j+TkT0Blbz+XTUbbPn49Dp2P0+AldMn9ofD9AUlbUOM/WxgX/xY6OyTPPa3W8w1ZHbp2B1Mje+eQHcO2oe6hWCV5aeEOPJsO7btB1lNpKqfI4oR12gIJFfye1ZCkAxZp43L8vhMcqSbmyaSzIqEGTuNi+gNsdX3N3RvcJjXqmpl3Ngot/4DeDzuMPQy9m6aXfkmI0cLDKd0F5sKwAh/owUl2OQavFLNO4f+zNJIVGHBcabo+kyuYipJcKDl92HHZgupSyRgihBVYJIRYBTwCPSykXCSHmAM8BZzUcKKXcD4wAEEKogTygPgvaI8BSKeUzQohHvK8fFkJkAFcBg4F44EchRH8pZVcko+w1OBxlbNlyDW53LfnHPiV6WtP8VDGpaSx4Lg2PR/LOmhycbg83TEzBoFXz0LkDeOjT7Tz3/X4GxAQzY1AbmXG9RKWNgJ2Qf2Arif2UQjyehX9GFSQx3LyQs1I7XrrVF/bt2oXB42HQ7Fltd+4AGnMosZpqVuYGUfaP/2PWlbejC4lkbVYVaUYn8cPOanV87rYVuFHTNz2tS9bnD4YPvpLr9nzEB9ZD2ObN5s8Xf45W3/268Tmpc3h2w7NYbWVYLL7b4Dwlh3ChRj6aRxgSdTN5wupRvhODUKu34nY7UXeha2xLhJvjuWXcCaEWrg+h0JsqxBfsLsVx4KKEB3ny7Bub7VNtU/qE9lLB0eaOQyrUl3/Ten+k96c+YssCtJU6dQaQJaWsV+hdCLzr/ftd4KIG7Z9IKe1SymzgIOBr8qVTloMHn8btPlEmszU/c5VKcPPkVG4/Mw2DVlEvhRi0/PPqkWTEhfDgZ9s5VumbcTIuTXGz9RSd2HFgDMbjFmi7WGh4PB7yPR4SVCrUzaQw9xfX33Y/ExNgX4WOt/7zBus+/xdWDEw5s20vm0N7t6DCTZ9RM7tsff7g/y75gjvN/fnansfDn87ye84oXzBoDPS19AWHFbS+71A9DitOtGj1RjT6tlNrhIdNQq12cCT3584s129YdEHUtBFk2hCbU9mN6TQtC4XKOkVw9NYdh0/GcSGEWgixDSgClkgp1wP3A38XQuQCzwOPtjHNVcDHDV7HSCmPAXh/1xfSTgByG/Q76m07bSkuXsyxgi8x6DuXddWgVfOva0Zid3p4dtE+pJTU2ltPTGgMDsMlVUzIfpW6ZV8CIE0xqNQS6eq8S2VrFO3dS51eT5+Ern17zdHJzLz1Ca6fM5Fyj5mfsp0k6aroM65tVcehggoStZXownv3R1CoNdx12RfcGzaKJZ5KVqx9vtvX8PXBr9lVuosgjxuMoT6NKfr6j6QULUaNG3udb/XF09LmICXk5v7QidX6j1BdCDVuibsF9Zy9robFs8Yw/64L2Lb5O2ps1egdEr2qZaFgcyrOLkatf7MN+wufBIeU0i2lHAEkAuOEEEOAO4EHpJRJwAPAWy2NF0LogAuAz3w4XXOP2k0en4QQt3ltK5uKi7smV393kZ3zGiZTGnHxlwMwZrQv/6bm6RsVxA0TU/h6Wz4XvrqawX/5gVUHSlrs73Z70HgrjWkX/5q6pR8jvKVAaSH2wV8UZWYCENe/c6lTfKXPuDmM9Tp8TT5jXJs5p6zVlRyzG+nbS10im+OmWa+T5lHxu/3vsnL9y91yTikl9y+7nz+t/hMjokYQ5HYpnlU+EL3tFQB0OKnYvdSnMVFRqdjqoqmtbd6NvLsJNYTiQlBtK2z2ePbO1STl1JL+0wH01z5I30tu4v0X3Ix+7HWKC5qPv3G6vd9Jde+KG6qnXe64UsoKYDkwC7gB+NJ76DNaVyfNBrZIKRv+ZwuFEHEA3t/1lWmOAg1DeBNpRg0mpXxTSjlGSjkmKqpnPEn8gdWaTXX1ThLir0SnU9xcdbrOXc8DM9MZmmDhcKlSP+O6t9bz3Pf7GP3kEv659AAHCqvZ4I1K/XLDMha4J3BQFYuUWowr78Ast2B19kMYW69R0FkcdYpg0gf7P0dVS8y6+ffceP4UBky7qs2+OVt+AgR9B/SuaPHW0OpMvDb7HYIlPLvnLdyerjcNrsxbydIjyk3/71OfRXhcJwIBW8PV+Ald1UZNjIZotMPQaI5gt/d8uvEwg5ISpLg2t1F74eG9LLzvEvL+9EcArH+8g/LfXktBPyUSPD6/gKxzz+WrP75IXW1j1fJxwaHpnRETvnhVRQkhQr1/G4GzgX0oN/P6tJLTgdYK8F5NYzUVwHwU4YP39zcN2q8SQuiFEKlAOrChzSs5RSkoXAAIomPmEhqqBCnl5X+C223v8Jx6jZrP7zyDDX+YQUyI4g302vIsSmsdvLgkk5kv/cwV/15LhdXB97tyeYRfU9QvBs+131BXG02tKwP977peDeB2Kqowlab7tuNqrY4UH+MxsneuA6C8qoa9P/yPVR88Q+bSD3Bauz7ZXWeIjx3JQ0mzOKySbM78ukvPVWYrO56n6slJTxKrDwfpAV3btgrrjhNrK9ImET5gos/njYk+C5VKkpX1fbvX7G8ijIqWvdTa+Pl226dvkLZ4L/GHayiK0jHi6ruZeNsfmbZwNYP27UXzn3epjEth4Of/Yc05F5C779DxsU5vvWNtV2di7iC+rCoOWCaE2AFsRLFxLARuBV4QQmwHngJuAxBCxAshjvuTCiFMwExO7E7qeQaYKYQ44D3+DICUcjfwKbAH+B64+1T2qJLSQ13d0eOCwGrNxmYv8B6TFBYuIDR0HAZ9LGZTP6Kj53L48BssX5HB0p/SKCtfS0HBfOz29pUK1WvU6DVqnr10GBP6nsh1YzGeuEk/+vly1h8xMyxyN0Ys6IeegfHvBzD/dS3q0K7PAhuVrGwsc3fv7vJzdYRgb7berzblMW/tEX48aOOjlQd56bmnWPHB37HVVPTsAlth8sjbUEnJpv3+K+V6Mg63gzPnnckLm18gzhzHBWkXQL2Xk7sF25q9GuenNyO/f5Tqw0rg6ebRLxD5yDbUWt9dnvv3n43breZYgW/qra4kwqgEmZZZC9lTsBKHS9k9OA6dEATWqSNQqxs7saZPGcc5iz6j5JG/Yakq4ehVV7H2M0UQ9nZVVZvuuFLKHcDIZtpXAU0ip6SU+cCcBq+tQJP0jlLKUhRPq+bO+Tfgb22trbdTXbOP7dtvwW4/hlYbQUjIUEq9eXfS0/9IqGUMVushkpNuBhRPqsEZLxITPZedu+4BPGzdquRQio+7gkGDFBfA3bsfBCSDBj1DccmPFBV+h0c6GND/cQyGxlHbZw2I5sz+UXy5JRd77ToSxYs4HMXcsuSfLNrjBExMTliPztO+aG9/0GfSJEIXfsvqffsYY7ej1feuWImpv/oDE2vLKMnciMOjIrLvMPJ3rWL9mpUsO1jLuuefZUKKifEX34HB0rvUpcGR/Rkg1WyuyOyyc2RXZh//+8oBV6ISKsVCKdQtxnE41ryBbo8SZR054R4AtEXbUanbF4tsNodityeh0TSf9aA7CTcp352Xtv+HAoeb29NncM/Ef6A9euJhz9y/+ZQ2Qgim3HgJBzP6kXfPPUT/6QEW/Pe/5F14HaA9dVVVATqG01nJzh13IqWTvn1/i1ptorJyKwkJ16DTRXHkyH85ePAZVCoD0dFzj49TqTRER5/LqJEfotVGoPEmPKuo3MyhQ/9gw8aLKCj8moLCb9ix8y527bqXouJFlJQsZfWayRzJffv4XLW1h6is2o4Qgonxm4hxPYrTWczwYa/z1c1ORkTt4P9GrWZg+AGCI7q2sllzqHU6ZowaSY1Ox0//frPbz+8LGnM4sSPPJXn0TExhMfSbcinXPvwPbj1vHAkmB8ty3Pz3n89SfmRfTy+1CcMMMez1tL9OvK8cqjzxRH1OnwapYtTaFpMcqo9tAcCBFvW6f1GFGe3o6zt0fpNpDDpdCZWVh9vu3IXEhaShRlLhVBQjm4uUErfBhTXH+ySPOavVOfqNG8bYpYs4fMUtRBcc5sx/Psq3X/8Ora1jOb+6moDg6AKklOzZ+3/Y7McYNvR1UlPuZtLE5Zw5dQsDBzzJsKGvYbcXUF6xjsTE69FqmxqHw8LGMXXKBs6cuoWMjBewWrPIznmF6uqdx/uUliolWceP/54xY74EBAcO/BWns5Ly8vWsWz+TTZsuIfPA38g9+h4AI4a/Q1TUOYzsfxFfP/goM/TKhzs8rWniwu5gyDXXkGytY2NRIRVHfI++7WkSxszhuv97gRtmDqPGreWtt9+mNHNjTy+rEZagOGoFyJqu8TocG3sicWCjGhVqHbhPEhwOK+59i1BnKlrsrzmHHBJZk3A7A4d3LAFhYoKSIv3gwYUdGu8vLMYYPjr3NZZcvoRJ4THsrCrj3PeGE1rlIfviDGKWLSJlcNt16k3BZmY98SBDf/6J6vAYVEiSPTVtjusJAoKjCygq+o6Skh/pl/Y7LJam9aktllEM6P8ERkMyfVMfaHO+2JjzMRpTjr+Oj7+KUIvyZYuLu4wgczqWkOGMHj0PgNLSFeQeffd4/9zc/1FdvZP+/R8jImJKo7mrqnehqlJh7tP9Ow5Qtuqzr70Gt0rF4nffbXtALyN10iXcfMX5eBC8+/FnVB071PagbiI8NBWPEOTk/NQ18xtO2M5izbEnDqi1x1VVVUtfpPa9q3H9PR31J4onWx4xqIdcQvHcdzjr+oc7XI61b98zcToNlJT2XGbgejJipxJqjGVmn1mY1SoGWbWogP6DRhAel9KuuUyWYNJ+rezCzDG9SwVaT0Bw+BmXq5rMA08SHDyEpKQbW+yXmHgtEycuQ61uW68vhJqJZyxl2ln7mDE9i0ED/8bw4W8xoP8TpPf7w/F+QWZFj7p33+8pLV1GWOgEBvR/Ar0+jpEj3iMpsbFKwOPxUGs6iqkmqstrKbdG3PDhpHs8ZNrsuF2tByz2RqIzJnL9xedSJ7V8+c6/8HRx4KSvnDv4OjRSMm/Ph36fu8JWwfD3hrfcQXqgKp+QlY9jPvQdGueJJ+f3uYRp06czduxYDIaOV7HUanW4XP2Qck+P5uhqyKXDHmLFtdt5IPIiACL6j+nQPOpQJXWfp9a3oMjuJiA4/MyR3HdwOIoZOOBJlPRc/kPVwDdeozGTmHhtIzWXRmMmKekmPJ46VCo9gwY9Q2LitUyetIrw8ElN5qs6tBp3qJswc89ndImMjMSl1eCs6t2uri0RN2wac0b1IccewuqPnuvp5QAQGd6PWZpwvqo9RLXVv4We3t/7fvMHpARbFehDwKXE6fzEGbyt//XxLmeElhIe7p+qdhbLGWi1tRw7ttkv8/kL22ElOsHcr/XMyy2hDlMEh7u8wl9L8isBweFHamoyOXLkP0RFnUNISM+oftL6/pZ+/R5h1MgPMRpbLoUKcGyrohqKHd5y9bvuQtQ/Map7Z4oFXxhx/q1khFhZdshG3rauUQ+1l/P7X45VJdi9++Qwqs5hc53IKpAeln7iQE2RYhi3JFKfBKJObSFj6kXUoMR2CB/TkfhCaopSNCw7p/mM0j2Fc38OnmAV2oiOqZrqdxoqU9dkpe4sAcHhR/LyP8LjcdI//U89tga12kSf5FsJDm47QWGJex36IhMhfTu2nfYr9UniNKduiRghBOff9FvM2Fm8eHFPLwcAS6SivrT5ecdxy9BbSAhK4NyUczlQfuCEa259HY7QZFAp7+Vc9/cMW3IZy0MuZUHor0mdfa/f1pGQMAy7PYSqqt7lmEBhLTLB1GEVsDAoKuyAquoXgF4Xg5QONJquTdXhD8r3LccRXUektmsKNLUbr+CQvTRS1leMYbGMj5ccthooL24+d1G3rsdrQ7P5oQZ4Q8IMYXx/6fc8Mu4RNCoNn+7/VDlQ4XWNLcum/IdnTqwDO2kWD+ff/yJJyZ0ve1yPEAIhBqJSZeFuKeiwJzBqEXm17Pu/i3DWVbd/vPf7IPQdtwF1Jaf2t7SXYTYrFeJqrb3Hs6Yljmx+BTyQOP6enl4KoNSahtbTyZ8qZIxRPNf2rV3UwysBg9cuVtdFyRcijZGcmXgmi3MW45EeiPQmrPz+YcL2ftCoryuuqYehPwgPPwONxs7hw6u6ZP6OIHQaVNUSOX8/Oa/9tt3jPTZFFagyBgTHac9xwVHbWtqunsdReoxSy3aCC+MJSuzamhu+UulwoHU60fey6PGOED5sFtGijH2ZXRe17St6oaiLbHRd1p7RMaMpqiui0l4JccPg0TxIVNzFNzGUQyQx33wNA87+VZecv29fpQjY0aPLumT+jqDtn3r8b9d/VpE3/5/tGi+0isCXvdTLMCA4/IjBkIQQOqy1WT29lFbJ+uHPSJMkZWj7n4S6ipK6OsKcrtNix4FGx8AoDUdqNNRW9Wz21rLaYwBoRdfZjvRedZjL473J6YPgqo85ZB5FVvzFhP1mJXMeeAVdFxXriorsj8MRQnXNli6ZvyP0/+N7DNizm8RFiit0XWb78rEJb5En6QwIjlOO6tV52DLLfe6vUmkwm1KprW1f3e/uxFVTSaF+OYYSC1FDL+rp5QCQ+cMPFHVRTfOeYtDwcUhU7O9BdZW1ppD/W/NnzB4PE9Iv7LLz5FTlAA0EB0BQFOsSbiW/VoXFYkHTxU4PKjEAtfoQrl4SQwOgUqkwJgxEaiS2xeuxFeX4PFZolf+XdPae62lIQHC0gONYLZULDlH2WSYeh+/bfJO5X68WHNmLHsMd5iGlz1294ul+x4cf8skqRTedGtX1GXm7i9gx52Ohmn17dvXYGt744S6yhIsXM24jMXF8l50nxqTUt28kOIBhw4ZRWVlJVlbX78DDwiei1do4dKjns+U2RKMzYbzvAlR5NrLu893tXdQL2l4kCBsSEBwtULXkMGhUeKod1K475vM4szmdOlsubnfXVs/rCB57HfnyO3SlZuLH/brtAV3Mrs8+45t9+wh1uXno3nuZ/bvf9fSS/IbQmxkY5iarUmC3dr9LZWHRbj6q2s/52mgmjv9Nl55rZp+ZaFVaXtv+WqP2gQMHYjab2bix611lBw28Eo9HzaHsFgITe5DU255Dc9VoVNtKqcne6tOYgI3jFMSeU4ltTykh05PQp4dSvSIXj823N9BsTgMk1l7oWZXzw1O4Il0kR9/Y47uNvd98w1fbtxPscnHTbx8gKKJJ5v1TnkEjJ+BGTeaKed1+7jeW/R9uAXdN/WuXnys+KJ5J8ZPYV9Y4Q7BGo2HEiBEcOHCAmpquTdYXHByD230GavUGiop7X6bi6CvuAKDwc9+yQNfvOAKC4xSiclEOqmAdQZMTsJybgqfORdkn+5GetvPhmE31nlW9S13lcTk5av0cbZmepCld+wTaFpmLFvHFxo0EuVzc9JvfEHwKl/5tjeSJlxKqqmX91t3dmkupqHAnX9Ud5gp9IglJvlfV6wxhhjAOVhxk3r7GQnLYsGFIKcnsBg+zkSMeAWDb1mfa6Nn9WPpPxpVuoO77Nb59FuoFR8A4fmrgyKvBcbiKkLMSUenU6BKDCT0vDdu+Mqyb2g7oMplSEELd61xyc5e+hDPaQWLwlahUPZfW48CSJXy6ahVGl4sb77kHS2xs24NOUVQaLRMHxHDUEcyRrd2XguT7zf/CLQRXTfx9t50zwqjsGP+6/q8syFpw/OYYFRWFwWAgLy+vy9cQEzMIt2scQrWakpLe9eAmhMA4bRzqPAdVe9uONznhVRWwcZwS1O0sBpXANDL6eJv5jDh0ycFULT2CdLf+tKBS6TAa+1Br7T0uuR6Ph9zS99FUaOgz45EeW8emDz7gkxUrMLrc3HTHHYQlJvbYWrqLEbNvwEgda1b82G3n/LZoExluFal9pnbbOW8fdjuvzngVgN+v+j3D3hvGvH3zqHHVoO7G/GPDhz8KSLZsebrbzukL2TvmY/vkZwCMCf3b7C909TaOgOA4JXDkVqONN6MynchEK4Qg+Kwk3JV26va0nfPH3Ms8q46t+g/2WCvxmvNRa3omwG75v/7FwoMHCXM6ueW+ewlPSemRdXQ3upAoxsTC/kodZfnZbQ/oJKWlB9kjHJwTPrTLz9UQg8ZAtCm6Udtf1/+VyR9Npra2lqCgoG5ZR1zcEFyusQixktKyrv9/+8r+g0/g0UlcWjWa4LZVsye8qgKqqlMCd4UdTVjTMH/DwHDUoXpq1+a3OYfZ1I+6uhw8nubrLnc3h/PeRF2pIvWcx3rk/Lnr1/NzYSGJdjt3PPYYloSEHllHTzH27MsQSDb90PVG8uwsJW5kYJ9pXX6uk9Gpmwb46d3Kg0p3CQ6AEcMfRag8bN7cO3YdLpcdTUg5xwYZ0DjdrJg9s0kfj8dD3pIf2P/qK0DAOH5K4bG5cJXa0MaamxwTKoF5Qhz2Q5U4C1t3rzSb05HSjdWa00Ur9Z3SPYuoi6kg2j4ZjaH7vrwNWfTNN2hdLi6/9140p1mgny+E9BvLQFMFW49U4qjo2sSHh4qV0sIpyVPa6Ol/+lr68tJZLx1//dqM1zC6lffbbG76neoq4uKG4XSOAlZQXt6z9cgBig5vQ6jAcpGSGiX2cD55S5TsySXr17P1rjvYNnYMVffej+eV16jKPoTwpt6pz1nV22hTcAghDEKIDUKI7UKI3UKIx73tI4QQ64QQ24QQm4QQzVYDEkKECiE+F0LsE0LsFUKc4W2f5x27TQiRI4TY5m1PEULUNTj2hh+vt1UcuUoWS11S89ltzWNjQSOoWdt6XIfikkuvsHNkb3seYYO+Mx7vsTV4nE6CPR4s8fE9toaeZsK0udiklk9efxqntQPZUn1ka/l+wjyS+PD0tjt3AdOSpnFxv4t5bupzHKw4SJw1DiEESUmt14bxN8OGPoJK5WLTJv96WJWXr+fgwWexWrM5dOgflJdvaHS8tGwVDkdjdXZlmaK2DksYgvZ3DwJQ9uBDbBkzmuIbbkT/0wrcZjM18YqjiMpoRBWk3IPcpf5Nh+8vfNlx2IHpUsrhwAhglhBiAvAc8LiUcgTwZ+/r5ngZ+F5KORAYDuwFkFJeKaUc4R3/BfBlgzFZ9ceklHe0/7I6hj2nCgTo+jQvONRmLabh0Vi3FLYa12Ey9QVEj9s5agv3UxmVQ1jxQAxR/ktl3V4iTSYqtFo8Hk+PraGn6TP2XC4c24dDdgvvvPQnSvev8/s5cg+v5EdnKdON8T0Wp6NWqXli0hPMTp3NzpKdRHuiiYyMJDi4e0sNJCSMwukcgeQnystz/TJnTs7rbNl6LYePvMnadWeTnfMK27bfdFxQFBcvZtu2G8g69EKjccZgpZpfTUU+Kddcg0utQuNw4jabsF84l7ivPmfYggUQHIQHMEZFoTKb0ERHY9vf84kym6NNwSEV6qN3tN4f6f2pr1tqAZoo/4UQIcBU4C3vXA4pZcVJfQRwBeDfEmUdwJFdiTbWjErfcl4d87hYpMODbX/LOazUaiNGQ1KPu+QeWvk4COg7/tEeXUdMbCwujYaSXpAtticZMffXXDFlAKVOI298PJ9N855D+kmYLlnzLNf/dCd64Napf/PLnJ2hylHFyqMrCXGHEBoa2iNrGDrkYWXXsblzuw4pJYey/0nWoeeJiZ7LyBHvERd7Kf3T/4THYyMv72M8Hif7M5VdvTjptpqUPh3pFhQXrEVjNJH6/SL6rl/L2BUrGfHs84QNGsyu+39D0P6D2IPNqNUahBAYMjJwZPceA39DfLJxCCHUXlVSEbBESrkeuB/4uxAiF3geaO7u1BcoBt4WQmwVQvxXCHGysnMKUCilbHiXTfX2XyGE6BZlreNoNfbsSgwDW6+FrEsMRuhU2HNaz3pqMqdh7UFVlaOunGLjBoKORmEZ2LPFmuIHDADg6I4dPbqO3kDGjGu48447SDS5WLjXys/vd96Au33XxzyU+T6xUs1/Jz1DQvxYP6y0Y9S56nhj+xtM+ngSNrcNg8tASEhI2wO7gMTEcTidw/B4llJZ6XvaoJMpKPiK7OyXiYiYRkbG3wkPn0RGxnMkJd1IRPhUjuS+TU7Oq9jtBQC43dZG49VqA566cNwqJZYlKCkZvSW0UR/PIUVAWO6773ibJiYGV3Fxh9fdlfgkOKSUbq9KKREYJ4QYAtwJPCClTAIewLurOAkNMAp4XUo5EqgFTg4kuJrGu41jQLK3/2+Bj7w7l0YIIW7z2lY2FXfynytdHso+y0QVrCN4auuxBUIt0CWHYD/UuuAwm/thtR7C4+kZr4icJU8ijZLkvt2m6WuRhJEjQUqO5eT09FJ6BZbYPlz/4NMMDa1lWbaDo1uWdGq+r7e/iUHCW5d/z8D+5/lple1nb+leJn88mVe3vcqAsAE8dsZjeBweTCZTj61pcMbv0GicbNzYPgFdWbWdgwefJfPAX9m3/w+EWsYyfNi/Uakae4717/9nhBBk57xCcPAQgoIycLoa3xvqaktRB5Wi1tIiqtpaalKTSbv+RM0STUQ47ooKpLvraql0lHZ5VXnVTMuBWcANnLBLfAY0Zxw/Chz17lAAPkcRJAAIITTAJcBxP0UppV1KWer9ezOQBTSJmJFSvimlHCOlHBPVyZQVVctycRVaCbskHZWx7fTPhvQwXIVWXBUtezyYzf3weBzYbP7Rr7YHj8vJMcd36AtMxEzumuI57cEQFkaIzUZhue8p6k93VGo15938CGZh4/sffuhwShLpcrDSXsQZ+ijMwXF+XqXvHCg/wE0/3ES4MZyXp73M5xd8zuzE2Ugpe1RwJCdPxOEYjNuzhMrKtl3p68nOfpnDR94kN/dtwsOnMnToqwjRNJDRZEpl7JivSEq6mWHD/o1OG4bT2VhwbF/7PABaVUqL55NCwElqS3V4BHg8uCt7tqZLc/jiVRUlhAj1/m0Ezgb2odg0zvR2mw40UehLKQuAXCHEAG/TDGBPgy5nA/uklEdPOp/a+3dfIB3osoyBzpI6qpflYsiIwNiGmqoewyCln21vWYt9zGbFq6UnDOS5P76AK9xJQuTVqHpJDe8YrZZCIfD0Ur/0nkAfEsGMjCiO2oPYv/LrDs2RufdzCtUqpiZ2X5R4czy57kn0aj3vz36f6cnTAaj03vC60xW3OQZnPIJa7WDjpid96i+lm6qqnYSHT+GsM/cwfNi/0elaTsJpNCbRP/0PGPSxaLQWXCftOKqqlYy4sXGzW5zDEx6OprCokQOJJkK5z/RGzypf7ipxwDIhxA5gI4qNYyFwK/CCEGI78BRwG4AQIl4I8V2D8fcCH3rHj/D2recqmhrFpwI7vPN+DtwhpWz5Dt1Jan4+CipB2EVpPo/RRBnRRBux7ihpsY/Z1BeA2m6uBujxeDhS/gGaCi3J0x/q1nO3Rlr//th1OnKWL+/ppfQqhl9wJxZRw/KfV+K0VrV7/Kd7P0IrJWcO77k0+YerDrO1aCs3D7mZWPOJ3GPV1YrbscHQs3WzlV3HSKRcSmlp2w9yFZVbcDrLiI+/ArW6fZkWtBpLkx2HRqvMERKW2twQpU+fJAw2B6VbNh9vU4cpgsNV2mW3vw7ji1fVDinlSCnlMCnlECnlE972VVLK0VLK4VLK8V61ElLKfCnlnAbjt3lVSsOklBdJKcsbHLtRSvnGSef7Qko52DvvKCnlAv9dblMc+TXoU0JQh/j+ARFCYBwahSOnEndV89HhGk0wen0stdbu9azKXfwqjpg64lXno9Z0TanOjpBx7rkArFyxgl2LF2Ovav9N8nRErTcza9IoClzBfP7aE7iddp/HllUe4RtrDhdoIoiw9Jy79Y+HlTxc56ac26i9tlYJlI3oBSnzR454DJBs3PTnNvsWF/+AEDoiwtu/i9NoQ3G5KhupHlVSEQCVZftbHCetdYqbalq/E3NF1AuOlh9Qe4qured4CiCdHlSW9idhMw2LpHrpEep2lRA0sfnANrM5vVtVVR6Ph6O2b9C4I9CvnELexo+Rbg+4QHoEQq0GtQNdcijh101DE9J9keQhMTHEOBxk63Rkr1lD7JIl3Pq3v6HuojrUpxKDzr6G2aUvsGivYP6/HuXCe55BpW37/zLv5z9jF4JfjevZneXRmqOEG8Ib7TaklKxfvx6LxYLFYunB1SnExQ1h564z0WiWc/jwGvr0aT7dvJRuCgu/JSJiKhpN+78fWk0IUrpwu61oNIqKzuEqQA0Eh6a0OE5oNAigaP06kmYpKi211435lLRxnPa4JWja/2/QxpjRxJiw7mjZo6ves0rK7gl8K9z9A7aQbKKrzkO4a/DYwpDOCKQ7GKQBj1ODdMfhOBLKscdXUbu5ewveXHXHHZyfnMwYnY4Cs5mlr7zSrefvzYy/8kGm9TOxvTKERW/+BdowljvtNXxevIlJGOnb//xuWmXz1DpqSatMY/Hixdi8KTJ2795NQUEBY8eO7fJ6474yftwTuN1adu1uOYtCefk6HI4iYmMv6tA5NFpFSDa0c7g8ihtuUvqMFsel/va3ABR+ckJzL7y2od4oOHrHO9qDeGwuVPqOpX02DY2kaukR3JV21Jamqi6zKQ2324rNdgyjsWsT+3k8HnKOvIJGFUH/S3+H+urmc0J5PB5KXv0OR54FZ34pjO7SZTUiLDmZ0TffjJSS3D/8gd3HjnFO952+13Pmdf+H7c2/sDZfT58lHzDknOtb7Hvo4LcUqQUP9O1ZoQFQ7awmOT+ZNflrqKurY+jQoXz++ecA9O/fdgrx7sJiicdguAyX6yN27vyEoUOvatKnoOBr1OogIiOmd+gcWk0oAA57KXs2fExt7T70YbVUHTGjUrV8uw0bNoJDIUGotu9k4zkzUZeVoa+tQwXkbttCbyt19ovfcXhsLoQPLrjNYRwWBRKsu5rXQR73rOoGO0fRnh+xGveTGHQDal3LiQRVKhV4P8D69J4poiSEIDI4GJtG062V8U4Fzr7pD8SoKlixcVer/5vqugoAIkK6NwdUc7g8LmqDFXvG1q1bee+99zAYDNx6661ER0e3Mbp7OWPCo9jtFnKPvojL1dg+6XbbKCpeTHT0rHYbxevRaJWQsx2b/kq56zUceqWAV/8MH6puDs5AZ7cjystxRoRTO240e9P7cCy159/jk/lFCw7p9IBLojJ0bMehjTahjTVR14J31fFkh91g58jJeQWNI4w+425us6+rzIHHWoY+tecKKZl0Oux6Pe4urkV9qqHW6hieFkex08jOZV+02K/GoTgXBOt73n4wKnoUhzSKx/z555/P7NmzueGGGzhw4AAffPABe/fu7eEVnkCnMxETcyd6fSkbNr7U6FhJyVLc7hpiYy7o8Pz1Ow672KQ0VI/CVZlM/xFXtzl29NvvkrFrF2M2bmL8D0sY9+772EcOw+rsfRlyf9GCoz5RocrQcY2dcWgUjsNVuCqbesNotWFotRFYu9glt2jPUmpNe4g3Xo9G33baco9VIp1VqPQ9U9QJIMRsBiEo64aSog1xuVzk5uayZ88eqnqhZ1dZWRmrvP+SonWfQm3zPvzVDsXVNUgf1l1La5HpydM5HHwYoRHs2bOHPn368Omnn7J8+XKOHTvGvHnzWLWq7XKp3cXIEb/GZkuiqup9ahv8fwsK56PTRRMWNqHDc2s0DZJcVExixoWfce7Fy9DqfAuCPDnuyhgcTF1112VT7ii/aBuHfwRHJFVLDmPbU0rQGU29q5RqgF2rqso+9ApqjYXUs27xqb90qhGqni0ylThoEBQUcHjTJqIHDuzy89lsNvLy8liwYAEVFRXH24cNG8ZFF13UKwIlS0pK+PDDD5ESrpl1Bv0WvwZf3Q7XfAonrc/lVp5CdT7ekLqS/mH9GRQ7iP2O/cgsSVZWFkajkZtvvpn4+Hi+/vprfvzxR1JTU0noBUW8VCoV/fv/gSNH7mDd+seYMf0VnM4KSktXkJT4q2YjxH2em2CsxQZcdWqmndf5UgaGoGAKD/WeaqL1/KIFBx5Fhyw9Hdeza6KMqCMM2PaVtSA40iks/AYpZZekui7au5wa006SVHejMbQdoSulBJUJVc/GZJE0fjzG779n1c6daL/4Ao/bjcvlwuP20G/8OCL9aFQtLCzkzTffxO12Y7FYuOyyy7BYLOzevZt169ZhNBqZNWuW398ft9vN4cOHUavVJCcntzh/eXk533//Pfv370en0/GrX/2KxMREUD8F3z4Ia16GyQ80GmP35kDTa068kT8e/pG3d79NYlAiF6ZdyMSE5l1O/Y0QgkfGPcLVxVcTPiic86LPY+LoiceTG5533nkcOHCANWvWcPnll3fLmtoivd9MDh4YiUb7A0VFe3E4tyKlk5jYjqupAHSGYA5+048x519CaGTLAX++Yg4Nw1pZgcfjRqXqvtrtbfGLFhyaKBNCr8ZxpArz6JgOzSGEwDgwnJr1BXgcblS6xm+u2dwPl6sah6MIvb5j52iN7Kx/otaEkHrmbT7199TYESotKnMrGde6AY3BwPmTJvHVmrV8vXNno2PGPbt56C9/Qe2niGO3243b7Wby5MlMnjz5eCRzYmLi8XiDoKAgpkzxTyJmKSUHDhxg8eLFlJQo9q/4+HhGjRqFyWQiNzcXp9OJy+XC6XSSlZWFx+PhzDPPZMyYMSdqV4z5NeSsgqVPQvo5EDP4+Dls3rLERq8R91DFIR5Z+QgWvYW86jy+y/6Os5PP5v7R9xNrjkXfQWOvrwyOHMzjEx/nqfVPsaVgC89UPcPUECWAzmAwMGbMGFavXs0555zTK+I6AEaNepIdOy9g84a/ER3nwmRKIzhocNsDW0EIgd4chK3GP+ql4PBIPG431ooKgsJ7PpCynl+04BAqgSbc0GL0t68YBoZTszofe1YFxkGN31yz6YSB3N+Co3j/SmpM20lS3YHW6FuwknWbEr2q6+N/IdZeMs47j8RRoyjctg1dcDBanY4969ezqqyMwz+vpO85TWszd4T6ehBms7lR+gshBLNmzaKqqooVK1YwYsSIDhcccrvdZGZmsmPHDgoLCykrKyM8PJzLLrsMm83GunXrWLhwIQBqtRq9Xo9Go0Gr1RIbG8ucOXOaeiAJAXNfhAM/worn4Ip3jx+qs1UAYNWZqaot4Hc//w6TxsS88+YRogvh7V1v89aut/jxyI/oVDruH30/1w26rksLPF2cfjGjY0bz0IqHuHvp3QyPGs5b576FSqhITU1l9erVVFRU9ArBseXbNfy8aTVR6anExq6lohL6pj7gl/+PwRyErbb18tK+EhwZCUB1aUlAcPQmhEGDp65ziff0qRbQqLAfqmwqOBokOwwPn9Sp85xM9oFXUGuDSJl6u89jrJsOAtGYx7fvycpVVUXBH/+ELTOT0AsvIOT889Eldt4rKyQ+npAGJWVNiYms/ve/2b11q98Eh9FoRKvVNrJt1COE4Oyzz2bfvn2sWbOGc889t+kEreBwOFi2bBk7duygtraW4OBgEhMTmTBhAqNGjToe/DZ69Gjy8/OpqqoiPT3d96A4UziMvx1WvgBFeyF6kDJfbQ1aKZn59fm4pAu1UPPSWS8RaVRuNLcPv52L+l3Ed9nfsalwE89tfI5IYySzU1tOtOcPkkOSeWfWO5zx8RlsL97OmA/GAGB2mpnFLIqKiujTp0+Xnd/ldLFh/s846uxMOG8qhtCm6tuDG/eyYMMSwjXBJDjn4uafAMR2Uk1VjyEoCHutf7wFgyOUCI7q0mLi0ge00bv7+MULDk24AfuBzqX7FhoV2kgjruK6Jsd0ukg0Ggu1Vv8auEoy11Jt2kyCuBWd0fdCOY6jNpBVGAb6bqQsevllSt/49/Fo5uKX/0nxa6/T5913MY0a2e61t0ZoQgLRNhs5dt9zNrWE0+mksLCQxMRELBbL8WytJxMREcGwYcPYuHEjkyZNIiio+d2blJLq6mrq6uowm81IKfnoo48oKChg4MCBDBs2jP79+6NWN9VFCyFISEjomHH4jLth/RvKruPyt8Faxris1Xw84jIWxKYRagjl3D7nknRSTEeMOYabhtzErzJ+xQVfX8CCrAVdLjgATFoTSy5bwozPZpAQlEBeTR61mlpURhX79+9n7NiuKTRlLa/hw9ffJc+hZHPY/I8dzDxjGkNmjjnu/FBbXs3X380nWGXklntvRxekZfnPiuAwGv2T78tgDsJa5Z9o7+CIEzuO3kRAcIQbsFY5kE43Qttx45Mmyogzv+lThhACsznN71lyszNfRqU103fynT6P8bg9QARCV95oS+6urqbkX/9Cm5CI5eKLUDdQ11i3b6f0dSUPpWnCeLRxcdSuWYursJDD11xDvxXL0cb4We2lVqNxOjs9zcKFC9m+fTsXX3wxUspmb+j1TJkyhR07dvDTTz9xwQVNnzwzMzP56quvqKtr/HCg1Wq56qqrGDCgC58GTeEw7lZY9ZJi69j2IXhcDBh3DwNi2t45qlVqJsRN4Nvsb3F5XGhaiWD2F9GmaHbeoNiurE4r4z8aj9asbfL/8xd527P54psvqXDXMHvkdCISo/l20bd8ufY71m3ewJjho3A6HKzcsZ5aj43rZ1+J0bsbSdz8EBq7RSkO4Qf05iDKjvnHzdwQFIxGp6e6tHdVAvzFCw7Uyg1USuiMdlMTaaRudwnS5UGclPvKbOpHccmPnZi9MWUHN1Bl2kgCN6Ez+64vtu/NRmjN6JJP3JSly8Xha67FfkBxGS586in6vP8eFd98g9Dpqf7hBwBCzj+fhL8/B4CnzsaRm2+ibus2Dp55Fmi1RN17L9qYaCq//x7rmrXo+vSh7/xvOnR9dVIS1sn8RpWVlXxSWMHuUWexd+0W+peVIaVkyZIljBgxglWrVtGnTx+MRiMDBgwgMjKSM844gzVr1hw3lGu1WsrKyli/fj0bN24kMjKSadOmYTKZqKqqori4mLFjxxIf33ySS78y4S7Y8h58fQcg4OI3GhnL22Jc3Dg+zfyUBVkLuDj94q5bZzM4PcrnzVntJDrJP5Hk1rJqqkurcDocrF+2hl3FB9AKDVeeczEDJg0D4O5h/Vjz5U+s2beJ+RsXA2ARZq4770r6jj3hAh6VPB17tv/yQUmPB7ufbBxCCIIjIqgu612p1X/xgkPt9S5yV9pRRXXcJ14TYQQPuCrsaCMbB+GZzenkH/sUh6O01YIwvpK172VUOhN9J97drnH2g0pNZF3Kicw3NatWYT9wgKAZM7BnZuLMzeVwg/KVAKjVx4UGgMpooM+HH5I1Zy7OnBxwOil+8cUT/YXAnplJ/qO/J/7pp2gv4VJSqFbj8XjaHV+RWVpOfHAQHy7+kVX9huFWq1kxYCQ/9x/O5ZuWUbZ6NatXrwZg+/btAAwaNIgLL7yQadOmUVNTw88//8yhQ4cYOnQoS5cuxe12M3jwYObOndtztSWCouG+bXBoGZgiIKV9deSnJ09nTMwYHlv7GAaNoVtUVvXUOmvReDR47B4ivcbezvLl/z7lYI1SXVNIwbCI/sy87jyCwk+obTU6DVOvOoeJ9mnk7z2MSqsmrn8y6pM0CyqzFo+18zvceg5t2UjioM55ZzUkOCKSmoCqqnehT1We2G37y9F2RnBEKjcUd2ldM4JDybFfW5vVacFRlrWZKtM64uUN6ILaFzXsPFYJBKPvG0vNunXk3niTckCrxbpuHZ6GT0laLUGTJuGqqCD6tw80mUuoVPT9+ivqduwAIbBu3oKss2IYNIigM88kc/wEKr/+mqgH7kfbznxFaX36kFNURMH2HcSPHOHTmMziUq5et4u8IOX91FuSEEg2nZHB5T9tINsYRITdxgUXXnjcLTQ3N5fMzEz279/P//73P6699louueQSBg4cyBdffMHRo0dJTEzk8ssv7xWeQBhCIOPCDg3VqrS8OuNV7lp6F4+sfASBYFbqLD8vsHkKagswuZTvlr/+j2XWCgBmD5/GgPGDCY1vWSBp9FqSR/Rr8bjQqZBOj99irVRqNaGx/tuFmsMiyN+/p+2O3cgvXnBoIo1ooozYD1YQPLnjUa2aCEVYuEqb5pU5LjisBwkLa640u+8c2vsyKp2h3bsNZW21SGmm5LV/ULP4hxMHnE40iYmEXXstptGj8NTVoUtJQRPeeildlcGAeZxyPeaTDJ7RDz1E4VNPcWjueaSvWtmu9CbxQ4bCT0spyNzvs+C4e9Me8oIsDK4upVSjp8AYxN1BKkI8bopR0T8/hz4OGyNHjmTkSMWg379/f2bMmEFWVhaffvop//3vf7nmmmvIyMggPDycI0eOMHr06FZtI6cSJq2J12a8xp0/3skjKx9BrVIzs49/PNdaI6syy++CQyVU9DHGMv7iM9vu3AZCqwKJt8RC5wWH3hyEzU9eVQB6kwm71eq3+fzBL15wAGhjzTiPdU4nqQrSInQqXKVNjX96fRxqtbnTyQ7Ls7dSaVpNnOda9EHt27l4XG4cxWpch77GceAH0GgIPucc1CHBhJxzDsbRo/2auyr0qispfu01PBUVHPvjH7FccCHVS5eijY8n/PrrUBlbzqkVk9YXflpKaVFRq+dwezy8vy8LlZTsNAST6rCy9AKl5oHLI9GoBP9YuJQacwST1m7D7nY3O09aWho333wzH374IW+//TaXX3456enpxMb2TPbgrsSkNfH62a9z+5Lbefjnh1GfqT5eI7yrWJ23mli18r/0l+CIMIeSU5WPvdKK3tK5tCvCqw6Vbonwwx3RYPafOy6A3mTGUWftsuwTHSEgOKj/wHTuDRFCoAk3NrvjEEJgNqVh7aTgOLTnZYROT9q4e9o17uOFb7H9yGbGHt1HxoFjoFaT9v0iv8RhtIRKpyN91Uoyx42nasFCqhYsPH6s+MUXCTn/PGpXrkIY9JjGjiVk9hxUIcGYRo3CHBeH1umksoUkhHnlFVy7aAVHw6KoMZy4aVwTecIbTKMSOFxu3narGXE0h+S6GjzalqPlY2JiuOWWW/joo4/46KOPmDt3LmPGjPHDf6L3YdKa+NeMf3HHkju4f9n9/G3y3zg/rWvqelTaK1l5dCUXqi5EZ9C16OrcXsZNm8j+bz5m5RdLOfvmTq7d6yCD2wN0fndpCDJj82PWZ53RpKTkcTrQ6nouMWlDej6zW2/A7elQFcCT0UQYmt1xAJjMadRaO+6SW3l4JxWGVcSIi9FbfLMZ1NmsvPbp8zxV+g++Na/EUHIMgLjv53ep0KhHpdEQ/+yzoFaDWo1h8GCMXjVR1YKFuCsrcRUWUbVgIUfvuosj111PyWuvkb97N06tFksLu5L/W7GefXF90HjcRFZX8FeTh7s1Dm4bPqhRv09XrKEwJJQ7I8xUaLS421A5hYSEcNNNN9GvXz8WLlzIkiVL8Hi6p3pjd2PRW3h71tuMjR3LY2seY2PBxi45z6f7P8XhdqCv0NO3b1+/JZNMGzmAPsY4Nh7Zjq2mc2ocUe9Z6fZPbRh/q6p0RuXhyNGL1FUBwUGbVTp9Rh1pxFVmazZpotmcjt1egMvVsRw2Wbv+gfBo6TvmPp/HvP7tP3i9TklT8UzsX8ifrtgjVvz1Hhy21j+EUkrcVVW4Kyo6VWwpZObZDNq9i0G7d5H6xeekfPwR0Y8+QvDs2aSvWc2AXTtJ+u9/0KcrEfYlr77GK+99RrFLx9dDx/J1ztHjc7k8HnbnHWOtIYQMt43dsyayfe4Ubhk/ij9NGYe+gQtvdWU1L1XYSS0u5Lyzz8TodKDy4Tr0ej1XXXUVY8eOZfXq1Xz++ec4/RBT0hsxaAw8f+bzRJmiuPmHm7nlh1vILM/02/xHqo7wxvY3mBExg7qaOtLS0vw2N8CUqVOw42LHkk2dmkeoT6iq/IGtutqvBcr03geo3mTnCKiqAJVejbu888VSNOEGcEvcVXY0oY3dNk94Vh3EYmlftHXlkd2UG1YQ47kcY2jrwXZuj5vHP3uUBXWLcQlFp39b9I3MPfcyOPcyFlVfTf+F29gxYQxFKRbcfeLQolY8qqqq0ZVbMVbZCasF4VLGq0NDCZkzh6j77kXtzfvUGSJuuAFuOPE6aPJkzOPGUf7Z5xQ++SQ3LPgMgMtHT+CL7BJe332Q/hXFLLfEUGwOAb2RPwxMaNVo/ccFS8iPS+GjEBVqnQ4j4Grly3zgwBKysz/B5SrF46kCUcPYcTbKytbz1lvHuP76WzCb284+fKoRZgjjo7kf8dWBr3hvz3tc8+01PDLuES5Nv7TT+vR/bv0napWaGdoZbBFb6NevZc+mjtB3/EDMiw3szdzHOKZ2fKJGqqrOUZRziNw9O5l89Q1td/YRvVlR7/nTbtJZ2txxCCEMQogNQojtQojdQojHve0jhBDrhBDbhBCbhBDNugsJIUKFEJ8LIfYJIfYKIc7wtj8mhMjzjt8mhJjTYMyjQoiDQoj9Qoj2JQ/qACqDBo+tecNpezjuWVXSjGeV6YTgaC9ZO19GeDSkjW17t7Fiw2K+si3C5DYyXo7gs+mfcO/sB48fn/38x1Q/cz8Fo/tgrLKTvHQvUct3Ydl5GENxNcWaOnYkeyi7aDLRDz9M9CMPY54yhfJPPiFr9hwqvvgS2QXqG6HTEX7tNcS/8ALMPAeXSs19894hzVrFXq2Jz+LTKTaHEG+t4o8WNTPiWq7CvGLDFuYl9OWaolymjR0BgFmjxdrKQ+CBA6+gUq8AkYtK7UGjiUUlUoiLO0Bc/Pu8/vrvKSjofXUR/EG4IZxfD/01X1zwBaOiR/H42sd5ZOUjON0d32nlVOawOGcx1w64ln0795Genu53l2aVSkVKaALH6oo7VRrBn6qqzQu/Qq3RMHym/+JkTJZQAGrKmi/q1RP4suOwA9OllDVCCC2wSgixCHgCeFxKuch7038OOKuZ8S8D30spLxNC6ICGLhAvSSmfb9hZCJEBXAUMBuKBH4UQ/aWUnb+zt4B0eY5/eDpDfSyHq7QO+oU2OmY0JqJS6dqds6oqdx/l+mVEey7CGBrXat8v1n7C0/ueI9QTzI/X/ITe2Hyw2riLboeLlMSI9VtqIQRF1iIe/ep8BoYP5NZz/9UoNUXEr2+m4PEnOPaHP1Dx+efE/uXPGLqgAJNl7hwsc+cw/+nnmfLuWwy7+UaGTRvJZ7v2c6jGyp9mTULTyk7D5nbzcG4ZsULw+HkzjrebjQbq3O6WPVOEGocjlDmzG6s9duz4mALPY4wctZDdexZiNH6GxTLKb9fbm4g0RvLGzDf4z47/8K9t/yI+KJ7fjPKhVnYzfLD3AzQqDQOqBrCudh3jxnXODb0lomKi2V2eReXRUkKTOxhc6FVV0QnhU0+dN526rhWvwfYSkaDkICs9eoT08d1TY6Ut2txxSIX6PZLW+yO9P/VhmhYg/+SxQogQYCrwlncuh5Syoo1TXgh8IqW0SymzgYNA13zqGuKHD406RA8agausOc8qNSZTWrt3HFk7X0JINWljWv8Cf7bsAx7L/BsO4eJvA/7SotBoui5x/Ea6KHsRVpeVxyY+1iSfkWHgQPp8+AFxTz2F4/Bhsi+5lIKnnsLdRWUtz/7N3ZSFhJL96qu4XG5uGDmEx6eMa1VoALzx02pywiN5TOMgKPiEB09wiAWPWk1deUsJLXUI0TRL8rBhV9Mv7Y3jr7dsvZ6Skp86dE2nAiqh4vbhtzMtaRpfHvjyeLqQ9mB1Wpm3fx5TIqewcfVGMjIy/K6mqqe0sASVFGiDO+5tdHzH4er8TloIgUrtXwuA1mAgODKKsvyjbXfuJnwyjgsh1EKIbUARsERKuR64H/i7ECIXeB54tJmhfYFi4G0hxFYhxH+FEA0VxfcIIXYIIf4nhKgPg04Achv0OeptO3lNt3lVZJuKizuXAEwTacRd5UA6O/fBESqBJsyAq6R5zyqljKzvgqMqbz9lup+IknMxhbfsBfXCJ3/liSPPMqw2neVnL2bq5I5p937I+YFB4YNItTRfuUyoVIRecjFpi74j9MorKH//A9ZNnsHj9z2P2+miuriMn9/8mAVX38GyCdP4cdJM5t/7RyqK2r/Frjt0EEt1JVlxSVz+42ocPuify611vOZQMy7nIBfOPafRMWOooiap8dY4z8vbwubN/zlxbaIGj6d5d92+facx7awDTJm8HrO5Hzt23kF+/mftvqZTicv6X0aZrYyP9n7U7rHbi5VULv1r++N2uzn77LP9vTxAqYGyr+wQ6ZZkzGEdq6MCDQRHJx8erVWVHNqykdDYOL9X6wuPT6Qs3z+JE/2BT4JDSumWUo4AEoFxQoghwJ3AA1LKJOABvLuKk9AAo4DXpZQjgVrgEe+x14E0YARwDHjB296czqjJOyqlfFNKOUZKOSYqqmV9ty+oLToA3FWdT+WtjTG1GExoNqVhs+XhdvvmHZG1/R/KbmN005Qf9fy86Ufesc9jimss/7v5Q8ITOxa0lleTx86SnT6loVBbLMT95S/EfPgx/9/eecdHWd8P/P29vbL3JIGQMMIGWQKCo4gDrVpx1b1arVq1tto6qm2dtbQ/cVStlbqxti6WyFJkhRH2CCSQhOydy11uPL8/ngsk3OVGBgn0eb9eeeXunuf73PeT3D2f7/czK42R/GTZW2wZexZF06cR9+ffk7BrE41xydgtEQxc/m/y58ylcPu+kOZz8Pln5SioUcPYbonhp4u/DRip8tKytTQajPw2KwVx8s5EJ/+PaZWbdm3Pv5u6+mfZs+dzAARHgY5lydujUqnQ6WIZO+Z9oqKmsGfvrzlc+EqPRs/0J6alTGNm2kzmb5nPu7vepd4efBHAPTV7AHCUOBg4cCDRASoQhEp9WQ0bP13Np397D4dwMTR7aOBB/mgzUzu7979sC5cd/aOLuzcfH8iKo7jffN5CCsf1mJlWAbOR42L+7Tn0Cb7NScVAsWeHArAIWZEgSVK5RyG5gb+3G19Mx29wKj7MYD2JxlOjynawrtvX0qWF46qx4Wry7iooN3WSaLYeCnidxtID1OhWEOuegynG925DkiRe3P4SKc4EXrz2r+gNXberLiuUq4deMOCCAGe2m+OALH45417+M/fnHBszlYLzfkzDCwsYvXk9l37xHhctXYTj5dfQt9oouO0ObHXBRYU0lR3DmLeNluxBPHbX7Vxee5RV5lgeWLqy0zFHK6t51xTJ7ML9nDVtsvcJ4kTkTH1DMXq9XDSusOhffPfds2h1zYSFBb4BaTRmRo18g8SEyzh06M/s2/8kveh+6zOEEDw15SlSw1J5YfML3LTkJhpbgzNLHmk4QrQhmqbGph7Pvi/ZXciC1xbw9Y6V7K47xJDwDEbO7p4l+3g4bjeDPlxO2azXk/6NNqKSU3DYWmiu7R9VcoOJqooTQkR6HhuB84C9yDfztkIxs4ADJ4+VJKkMOCqEaGtWcC6w23Ot9p7ey4GdnsefA/OEEHohRCYwGNgYmlihoUsLQ5tioWltSUCN7qyzYd3ReaVKXZq8ZW4t9r5Jtg/JDUTBtr8gEGSNu7/Tc7Ye2sxhTTE3JM7DFGTrWF/YXXY+2vcRI+NGkhoWfGJgk92JW6gYc8MVXPru35j7t6eZeMlMdLoTJp8xF05n0dSbSW6sYMe9TwZ13eJPPkbrchN/880IIXhl7hwm15TykTaCzQd9/+2e/fYH3ELFo5NG+Tzu9oQWq3Radu96R5bbHo5Ol0eL7S1aW6PIzr4qqPmpVDqGDXuBAel3UFLyL3bsvBeXq/u71f5GlCGK/8z9Dy/MeIGDdQfZcGxD4EFAZUslCYYEnE4nxh6+if6w4jtcksRNl13Hrx76FfN+eROq7ibv9lBUlcsp+8jUfioUdJXoZPl72V/MVcH8xZOAlUKIfGATso/jS+B24CUhxHbgj8AdAEKIZCHE1+3G3wu85xk/2nMuwPNCiB2e12cim7uQJGkX8DGyglkC/Lw3I6pA9k2YRsfjrGrB3dy5M1Byuil7dhM17+3p1KylTbWAClqPeJfLMBoHIIQmoOJoOlZAtW45sc45mGI770p20FMxc/LwbsSwA+/veZ+SphLuHXNvSOMsetkJWN/i34E6fM55fJE5BfOmrzm2Mi/gda37ZLNW/Ew5KkqlVjN/1mQ0LhdP53snqB04XMR/YhK54thhBg/3vWuQXPKXWqXVUVf3DS0tsRiNcoE/l8vA9GlfkZgwOuDc2hBCRVbWIwwe/FsqK5eybduNOBzd6yTZH1EJFZnhvn1enWF32TFJ8i6+p2srVdRXkaCLImP0YEyW7tWoaqNtx0E3FUdbVrzb2b1W1L44oTj6h4M8mKiqfEmSxkiSNFKSpFxJkn7vef07SZLGSZI0SpKkiZIk5XleL5UkaU678ds8voiRkiRdJklSref1GyRJGuF5/VJJko61G/MHSZIGSZKUI0nS4p4X2xttovwhdJR1Xuyw9Kkfjj/2FTkFoNKp0SaYaT3qva1XqbQYjRkBa1YVbHsZgWCQn90GQHFrKUISREV23YZca6vljfw3mJ46nUlJk0IamxplwqLXsKPEv/37pkty+GzIhTTrTJT86Xm/5wI4jh6lVadF3y7ZMD0qiovstWyITGT94cIO5z+3djNql5uHZ3Xe092lPcRZExex/vCj6PRHMRgmMnnS79BpryN3+N8xm7vWxTA97WZyh/+VhsbtbM67Cqu1qEvX6c+4POs2lQhuZa9X63HbZLPPihUremweNQXlVDnqSUrq2cZZJ/I4umeqMlhka4OtqecjDS3RMWj1BmpKjwY++RSgZI57UEfK4XyuBm/fhOSWKHn0u+PPzRMT0Wd0nsykSwvDmi8nJQlVxxWXHFnVuaO4sXQ/VdplxDovxBw3wO+cCxsKSXLFEhnR9R4fG8o20ORo4o6Rd4Q8Vq0SjEmPZNNh/yttm91Jtc7I+vRpnHdwCRV5+4gf56fVamUVzqhIr5cfnzqBxZv281T+ARZnZgCwc2s+X6dkckN1Campndu6Y831VOhbALmdaWbmpRgMYUyb9vtAYgYkIeEi9PoE8nfcxea8Kxk18vUzKtejzXx738r7mJI8BbPWTElTCXannfTwdAZFDuK2Ebdh1soBk3q1nrrWOoAu+TiaqxtorGwgcUhHs+n65d/hRmLiBZ0vELrE8czx7u04DGFydkJLL4SoCyGISU2j+uiRHr92V1BqVXmQWuXVhq9EwIZlhccfC6OGiIsG+r2WLj0MyebyGZZrNmdhtRbhdvs2dR3Y9hxCUjN4/EMB59zsasYszD1iDjCou9bZbtLAGPaVN1LT7K1wAXbuq+aKZ1bRKiB87mVICIr/7j/EU9fUDHHeyVzJ0dFc2lLF1vA41hTKX6Dn8g+gd7Ty4Pn+zXV6IUdZRRnvQa2+ksyMni0lHhk5nvHjPkGjCWPL1usprzglG+VTQowxBoPaQLQhmnp7PQV1BYRpw0g0J1JQV8DbO9/mhsU3UNpUyuayzaw8spJG5JvntGnTQn6/t199k9c+fJPFr/+bqiNyaf1Wq538sr1khqUQl9qzPe7bFnfd9XFotFo0Wh12a8+0jT2ZmLQBVB3tHztaZcfhQWg9OvSkz469sJ7GVSfsirE3D0el8x+jfdxBfrQRbXxHO6zsIHdjtRZisXRcddcc2kytYRWJrnmYYjsPDQV5FXjUVUKWYRDv7HyH6anTGRjpX6H5IsUsp8iUNpWSE+1nF9AJY9IiAdhd2sDZg71v9o9/ks8uh53xYSau/ulUdn06Gs0PS3HaHkVj8E7actrt6Fsd2DtpMfrY1Il8uWk/v99Zy4s2O8vTB3F75VHio/yb2STPP3ZI5kWYErNDlDI4TKZMxo9bRP6OO9m58x5sWb8mPe22ftNDoaskmhNZf+161J3kJqwrXcdDqx5i9qezkZBItaRyV9pdbDy0kbCw0PIrmuoaqXbK/sENx/LZ8HY+5w+dhgBsOJg8JTRzalBo2oocdj8BUKPT4Wz1vYjqLnHpmexa9Q1NtTVYono2xDlUlB2Hh7bkP3FShIZ1+4nkwoiLBqJPDycQmjgTQq/26eforGaV2+1m/+6nUTnMZE160GvcySx8fwHHtFVUamp4Ke8l5v53Lg+sfIDVR1cHHNuetiiq4qauOd0sBnntUWP1/WUparYx1mhk0WMzMeg1RM67Gp29noI3PvV5fnNxMQJQd6I4EuPiuNxayU5zFPfsK8bcYuW+c3yE356EW8jzU6t6PlSyPTpdNGNG/4v4+DkcPPgs+/Y/gdvd887SU01nSgNgSvIUFs5ZyMy0mdyaK9e8StLKQZOh9t84uucwAPOmX8atV95Isj6W5XvWsmzPWmK1EWRN6rle3m2IHjJV1VeU43K5cLb2ToRdYpa84Ck72HMVjLuKojja6CRrVG3RHX9s2xdcDLVQCXRpYT4Vh8mUCai8FMfRje/RbNpJuulO9GH+VxP1hyv5s+MNAEqlchJMCUxOmszq4tXc8+097K3ZG9Q8ASL1kZi1Zo40hG47bWl18ehnOwjTa5iQ4d3/3OVy0+h2kxBxwgyWef3F2MISsS78O45m7wCDZs9WXOenT/mdkycCcDA8isuqSomND1yjyO2Uk7M05siA53YXtVpP7vD5DEi/k5KS98jfcSdOZ++YL/oLgyIHMX/WfO4fdz8mrYlGj50/FMXhdrnJ+34jQhKkDc8kLTeTebdfj/DkBF966dwe6+fRnp4wVW1b9jVv3nsrKrWKMbN7pylWfOZAVGo1xw4E//3uLRTF4UHo5D+F/UjHm7028USFFMkefFSwLi0Mx7FmJEfHMWq1AaMxrUOxQ3tjDYfrXsbYkkXmlDsDXnv1lmW4hLxD0qq0LDhvAW9c8AbLrlyGUWNk4e6FQc9TCEF6WDpHm0KP1nh+eR47Sxp4+erRJEV4r+Rb7E5aBUQZT8S1qzRqIu59EENjGbsefcl7jKckiCGp84KO2cmJGFuaEZKbuyYGV6Le6bRCK6hMPdOBLhByuO6vGJLzDDU1a8nbMg+bveyUvHd/oKmpCb1ejzbInAa3y82SNz7jYFMxUweNw5wgB5+Ex0Zy1423c+PF15I+InRTbFBouh+Ou+KtBQBc+dgzxGf0zjy1Oj3xmYMo2benV64fCori8OCokFek2riON8D2N35tUvD9GHRpYeCWaC3xnQjYfsex7/vf49I2kJPz+4AF0iRJ4rGmZwFINCbw4cUfkh0lb2FjjbFkRWZR3RJabai0sDSONoSmOHZV7eLDArn433vbfZvHjq/kTtrNDfzpxTQPmYp22QeUr8vvcKz1mByVbQrQoXDkvu2M27GRwTnBNQdyuayonOKU+xtSUq5h1Mi/09JSxObNV9DY1PerxVNBU1NTSP6Nr15fxMbyHQyLHcS5N1zU4VhCZjKZ43vHLwXtdxxd93EMP0eux9VbSqONlCHDKSvYj7OPm4spisNDW1SVLr3jh73x+xPVTtQRwVfgbAvvdTd5/4PNpiys1sO43U6O5S+mUvcFcY5LicmaGPC6rmYHw63yzfKTSxeRaO4Y7mjUGLE6Q+sUlh6eTmlTKc4gbfG1tlpuX3Y7KUlHiY0vYOV2M/f8+xMOHCtng8dGDbB1t5xhnx7rrXCH/PUZ3CoNRc//X4fX3c2ySUdt6fymI4Tg9gNbOG/t50EnW7ndLQhH33zcY2JmMG7sRwDk5V1NdfWaPpnHqaS5uRmTKbgEvZ3L88ir2M3wmEFccfe1pz6YoAcyx5MHyy0GGqq6V3A1EClDhuFyOCg/1Le9YRTF4UGbIH/I24fQuq0OHO38FOownde4zjheaddHOQSzOQtJclB7bD37Sh9F35LG0BnB5RM4y6y8WPQgGyesItIQ6XU8zhRHhbUi6HkCpIel45ScHGs+FvhkYEv5FhodjTw3/VlW3XMniXEVfLnRxFPPbmPz/MM8++gaXn9jG2+9s4NhrWp+dJZXcWMs6Ym4J52Ped86qnef8K8Y0uVM+aYC/1+MuAGZuNQqKvO3BzVnFzZUzr4LIgwLG8r4cYswGlPZnn/bGV9d1+12o9EE/nu73W6WfL+caHUYc2/7id+ujr2FEAJUolumqoaqSoRKhSW66zlVwZCSMwyAkr27evV9AqEoDg9uj/9C6NS4rQ6cVS04q084b3WZ4ZjPCj6ZyVUnj9VEeu9S2mpW7dz9C9zqFnKH/wVtkLWmVCb5y9hZ74B4UzwV1oqQqmimhcmhv8E6yFcVr8KsNZMbm4tFp2f1fddz2SQbOmMhAIYaB84tNYxv1XCRVUdlvu+ggoz7b0cluTg8/+3jr1ly5JBg6wGv0mcdSB45GoDCtcFFkbmEHZWr52sIhYLBkMS4sR/9T1TX1ev1NDR4l905mZqSSpqwMTZ7FDpj13tqdBehFt0yVQmVCqSeL7FyMqbwCKKTUyneszPwyb2Iojg8OCtl847QqTn23CbKXtxM07p2RXlDXI04yq2gOtFOtj0mk2xqcqrryTDeT2TG6KCvq/KEv7qtvk00CaYEHG4HNbbgq2gOjhqMRmhYf2x9wHNtThvLi5ZzXvp56NXyF12v0fKXy65g0nB5tXXRoyMZeWsOxnHy881fFbLqfW/bfsSIbGyDxqL94WtaamUTVWTuCCSgpaDA7zxSZp5LuNPNlnUrcToCx827RStqqe9uTG1oNJYO1XX373/qjKyum5mZSVVVFfX1/svRlB2Uw8ATU/x3t+x11Kpu7TjCYmKRJPcpqV6bNnwEJXt343b13edGURwenFU2hFZF69FGJLsLbaoF69YTJh/D0NASbhzlVjQxxhOJhe3QaMwYrBlENEwjY0popT7anPiaON/5CG2O8o1lwRcUjtBHMCVlCosPL8Yt+V915ZXn0exo5kcZ3s2iomJlv0R9QznTJqRwy+2j+PHDcumNXWtKqTziHZ6ccMfN6Fob2fe3DwDQh0fQYjHjOuDfVKUyGJg443ysSGz5+6sBZXSrHajpe8UBJ6rrpqffTnHJQnbuvO+Mq67b1vGvIMACoOyoHGmWONR/wmtvIzTd23GEx8gh4fWV5T01pU5JHZpLa4uVisLA7Rl6C0VxeHDV2pAcbuq/PozKoiXu9pFEXZl9PAtcn9l5bSpfOCusaOI7dw4OrvgzqUfuDTou3W130rT+GHWfF4A4kZ1+MuMSxpFgSuDrQ1/7PN4ZczLnUG4tZ0v5Fr/nrT+2Hq1Ky7iEcV7HsrPlL//+fScitJIGRTL+ogwAPv7jJhpOKsOSdOm52KNScH71CU5PBJtLq0HlDryayr3r50Q53GxctZzDX35Ba31dp+e6tS5UomeqqfYEQqgYnPVrBmc9RkXlYrZtvxmns3fa8PYF8fHxhIWFsX+/d7Ja5eEyvv3X17z6zHy+O7QJAL25ayVvegqhFkjdaOQUnzkIlVpNwebgSs93h9RhIwA4untHr79XZyiKw0ObqdmQHUXcbSNQ6dWYxyccT+JzlAafwCVJEq46O5qozr8MujgLrkpb0O0qG5YVUfefg7hqbOgyIjote6ISKs4bcB4/HPsBm9N3BV9fzEybiUFtYPFh/zWW1h9bz+j40Zi03jfh4RnZ2DTNlBd2NE9MvGQgsWmyD+dYQV2HY0IILFdeg7n+CAc/lHt565qtuKO8EwpPRqXTMfnCudgF/Hvh67z3s1t9+gwkScKtc6NW9x/F0UZ6+i0MH/Yy9fVbyNsyD7u991espwIhBLm5uezfv58NGzZQXV2Ntb6JN/+0gFf++RprDm7EJbkZHT+EszPHozUEH3jSK2hU0I0dhykikqwJk9m9dmWv+60sUdFEJaVQ3IeKQ6lV5SH66hzcLU4vZ3byk5Mp/+tW6j4vQGXRYhoZuE2t1OJEcriPh+T6QhNvQnK4ZQUT7X+1ZS9qoOn7UtQROqKvG+pV/+pkpqdM570977G2ZC3nDzg/4HwBTFoT4xLHsaOq8w9jdUs1e2v28osxv/B5XKPWYI+uR13mLffws1NY/cE+Gqu9TTKZd85j9zsLaP7XQhyXTUTf6sCZEFwhu6G334k5K4stHyykoL6KolXfkuHp49GG225HMoDG3v8UB0Bi4qXodDHk77ibzZuvZPTodzCbg8tP6c9MmDCBvLw8Fi/uuBiZkjaGMTPPIm5gH/s12iFUottFDtOGj2T/+u/4/qN/MfisySQMzOqh2fl4r2Ej2LtuDW63q8f7mweDsuPwoNKrfUZAqQwaEn85Dm2KhbrPC3BbAyfeOOvkm6O/vI+2m3+bU74zmvPKqXxVDjmNvnYo+vTw4w7yzpiYNJEUSwoLdy8M6LNoT2Z4JoUNhbg6MROtOCL3VpiW2nnFU2OsGlNjJDZbRwURHisrx8Zq74rBaosZ9ayLCS/azKEPvgRAP8B/Sfn2pM88lwsefwaNy83m997xWvG5WhpA3ft1qrpDdPRUxo59H5fbTt6Wq6mv39rXU+o20dHR3H333cyePfu4zwPg/Fsu7VdKA4AeCIYaOGY8hrBwNnz2ER/87qFe7daXOkz2c1QWFfbae/hDURxBIDQqoq4YjNvqoO6rwwHPd3kUhy9F1Eabc9tR4X0jPX6dejv1SwsBiLlpOPoBgQssglyQ7pbcW9hasZWn1z9Ni7Pz92jPmPgxtDhb2FS+yefxpYVLGRgxkJyozqvo2iugRd+IRttxFRSdLCcBFu6oxu3DJJD54B1IKjXuPz8NQOyk0KqgmlJTGRgTT1FjLR9cfxWtjSdCQR1WuV+IWh185n9fEB6W6ynNHs6WrddTVdV5j/XThaioKCZNmsR11113/LVPf/cPrPm9myjXF4THxXPnq//kppcWoNJoWfPe24EHdZG49AwAavuoI6CiOIJEl2whbHoa1rxybAf8Ny5y1Xt2HH4Uh9qiQ2XSdLrjkBxuyudvwd3QSuwtuRiHhBbVdVX2VdySewuL9i/i2q+upbE1sOP1rES5EdKBWu8cCofbQX5lPlOSp/iPVRfgNNvQnFQ6xRJlICrJhLWhlX/+Zh3uk3w7xvQUbNMuP/48ccbMgPM9mR+99DdGDxzCMaeNd26ax5KH7uPIt9/gtHkUh6Z/Kw4Ak2kA48d9jNmcRf6OOyktXdTXU+oRhBA8/NDDANhEK40r+0cnu55Go9USk5rOpB9fTcHmDRzeFrhVcleISJBzyurKgkva7WkUxREC4eemo4k1ypFNfnDW2UEjUJn9J5xp4k3Hw2tPxn6kAbfViW5AOIbswI7ikxFC8MC4B1hw7gIO1x/mhU0vBBwToY/ArDVT3Oi9ijlYexCby8bIuJF+r6EKc2FoDMflY1cx77GzSMgMx9rQypLXvX0pOb+WCzxWWQzsXBV8OHEbOksYs/74AmMGDEalVrOn6CCfvP4X1s6X29xrNKH1hugrdLpYxo5535Mo+AiFhQvOiERBs8XME088wSWz5uA41uyz0VlfIbW6fTZx6yrjLpqLOSqanSuX99g126PVG7BERVNX3jeFMxXFEQJCq8IwLAZnrf+Ye1edHXWE3qtt7Mlo4004O1Ec1k3yByLqqu4Vd5uWOo2rsq/iy0NfBkwKFEKQaEqk3Ood2VPaLCdDDgj373tIHGJB7zCxJd+7gqdKo+LyB8dgsGg5vL2Kj57pqBwsmSnELl1JXs4Ilr3+HAVbQs+OFUIw6/mXue3jL7j9xQUMMFgo9nSjU2tPTWXcnkCjMR9PFCw49BL7DzyFFIK/qr8ihMA8Rg58aN4aWmmc3sJtc8pBKgk9Fzyh1mjJGDmG/eu/o/JIYdDjJLc76EVCZGIydeWlgU/sBRTFESqSBG4Jd2vneQauOjuaIAoiauJMuK1OXE3emc8uqxNtqgVtbPcdulfnXI3D7eDzg58HPFetUtPs8A49bqu4G2PwX4tn1pSzcAkXeZt891VXa9Tc8IfJhEXrqSpuIm9px1aYcQMSueK3zyBUBj5/8WnqK+oCzrkzLAMGMOuXv0FrlLPsHeL06olxIlHwNoqLF7Jr9y/PCOWhidQj9GokW/9ocGXbKy+o9ANCy9UKRO4556MzGnn34Xt4/a6f8uX856mv6HyHsGftShbccT1/vfFK3vjZzdQE8F9EJCT23x2HEMIghNgohNguhNglhHjK8/poIcR6IcQ2IcRmIcRZnYyPFEIsEkLsFULsEUJM9rz+gue1fCHEZ0KISM/rGUKIFs91twkhXutBebuNcXgMuCUq5m+htdS7ZHrz1gpajzZ06OPRGdp4WSk4fTjI3Q2tqAOYuoIlKyqLMfFjWHRgkd/VzO7q3eyv3c/EJO8qvQ2tsrM5Qu//y5Uam0xjTBmN+zt/H51ew6TL5HDT9Z8V0HJSv/K0oelccOfDuF3NfPrsK37fLxDRo0aRlS03hap29F2mbVeREwV/w4D0Oygv/8KrAdjpitCITuutnWqaN5ahjtSjywgu+CRYUoflctvf3mLmTXeSOmwEh7Zs4v3fPkRR/jaK9+6iuviEn6fySCGLX3mZqIQkBo09i8bqSg5sWOf3+paoGJrr/Ptbe4tgdhx2YJYkSaOA0cBsIcQk4HngKUmSRgOPe577Yj6wRJKkIcAooM2GsRzIlSRpJLAf+E27MQWSJI32/NwVoky9ij4jgthbcnE1tNK4qqODr2nDMWo/3oc+M4LwH2UEvFZbZrnjJAe5q96Oo6zZr3M9VK7KvoqihiK/pUhe2fYK4bpw5uXM8zrWVnJdo5Kd3pVH19FYuoWtW95gy4Kx7F735+Pnxg4xYGqK4kBhkdd12sg+K5GMEfLu5eAmb5PF8OmjMUaMpbZkfbdLK6ROvAEAffjQbl2nLzF7+tOrVH2cKNdDqKMMOCt71schuSRai0PLvm8tbcJ+qB7LlOSApuWuYAwLZ+yFl3DRLx7muj+8hN5kZtEffstHTzzCPx/++XEfyNbFn6PWafnxb57i4vsfITErm73f+y/gqdZqQJJwB1FloacJqDgkmbaltdbzI3l+2lR0BOBlbBNChAPTgbc812qVJKnO83iZJElte9X1gP/OPf0IQ3YUhqHRtBad+JA2ri2m7rODGLKjiL1pOCp94KQcdYQeoVV5+TlcDfIKXNMDZqo2zh9wPuG6cD7Z77ucd35lPmuK13Bz7s1YdN6+gLZ8ELVQs3frP4h660LC3pjJmM8fZmxFAYOXPcW+HXK9qWlT5a58a9b5L1/S4Kk+bInyVpDlhxtwMxGtwcyKt1/rlnNYo5EVtOQ+fS2zbeVINJrTx0/jD228CUcPKg5Xs4PKv+dT8X/bQgr1bfq+FKFTYR4fXMJpd4hJTee6P77MzJvu5OL7HyE9dxRLX5vPdx8uZM/3q8mZPA2Dp9Vu9sSpVB0twtrQeZFItUa2SLiC7EnTkwT1TRJCqIUQ24AKYLkkSRuA+4EXhBBHgRfpuGNoYyBQCfxDCLFVCPGmEMKXDecWoH16aabn/NVCCJ/ZZkKIOzwmss2Vlac+Jlw3IBxXvR1nnZ2Gb4qo/+owxhGxxNwwDKENLpNTqITPyCpHmWyLD7U+lj8MGgNzs+ayomgFVS1VXscXbFtAlD6Ka4dc63O80+1ELdTs2vY2kV8/gkMI1mRNZWf2LGruXEmtVgfLHgPAZZVv8u4AVV+HTJZDCksPen858lcWozeHMf26myjdt5vVC98KSd72OFzydl4letYUcSpxHVccp0dkWCC0KRbcja3Hk2W7iuR007i6mLIXNtPqKaJZv6QwKDOYq6kV67YKTGMTUJlOTcl9vcnE2AsvIWfyNC5/5HGGTJ3Bhs8+wmm3M2LmBcfPS8ySg2LKCrxrfbWh8vQuCbaZWU8SlOKQJMnlMUmlAmcJIXKBu4EHJElKAx7As6s4CQ0wFnhVkqQxQDPw6/YnCCEeA5zAe56XjgHpnvN/Cbzv2bmcPKc3JEkaL0nS+Li4wGVAepq2ZLya9/fQ8M0RTOMSiJ43BOGjcZM/tHHGDlt2yeGiflkhmnhjUH6SUJiYOFFu2NTUMfbb6rCyrnQdV2Zf6bMGFUCLs4URLVZy//tLEh12ii3RTL/+a3Kv/YzopLEcTB9PVmM1LkcLy96Vo6GmT/YuhNie3OkpIGDn6mJamk9k5DfX2SnIq2Do1CRGnSd/mfK++g/7fljbJbmbPX1GwiIyujS+P+B0NiGEDpWqf1T47S5t35/Ww/7LrvvDfqieY89ton7xYfQZ4cTfM1o2I9fYaFofOL/Btr8WXBLmCcH32elJ1Botc+55kPPvuJfxl/yY5JwTptSETNkHWHGoY+h/6f69bFv2Nav/9TZOu6x02/tKThUh1aqSJKlOCLEKmA3cCNznOfQJ8KaPIcVAsWeHArCIdopDCHEjcDFwruSxRUiSZEf2qyBJUp4QogDIBjaHMtfeRptklsuwH2nEPDmJyEsGdclGqok3Yd1WidvuQqVX0/h9Ke5GBzHXhK6EAtGWuPfC5heINcYe71He0NqAhMTo+NGdjq1qqWKgp8/x1ql3kTLimg7H9VGZqFnHe3+/F0P9tYiJleQMnOV3Plq9hlHnprH9m6P846G1TPnxIEafP4Cda0pwSxIjZqQiVCquefpFPvjdQyx59S8YwyJIz/WfS3Iy1uYSUEFk3OCQxvUnnK7GM8ZMBaBNtqAO19GcV45pTHzI4912JzUf70PoVMTelot+UCRCCCRJQj84koZvijAOi/FbB671aCNCp0ab1HeJoUKlYuS53i0KdEYT5sgo6itP+P8ObPqBL1760/HIOq1BNmUf3ZVPcvaQUzNhDwEVhxAiDnB4lIYROA94DtmnMQNYBcwCvNKNJUkqE0IcFULkSJK0DzgX2O257mzgEWCGJEnHbTWe96uRJMklhBgIDAb6XTiMUKsIOzddru46PaXLnb861KxSCRqWFWEYGo2uB81UbRg18gdta8VWks3JNLQ20OSQ3VdpYWk+o6naqGqpYrrDgQMYNv1R9CdHVxnlJMXY/enUqmzcdPWlQc3p7CsHY7Ro2fjFYb7/dwHpubHsWltCxohYIjxlWZKzh3DX6wv55OnH+OzZJ7nyd38gJSd4R7fNVoZLrSI8pp/VRwoBp/PMUhxCJTBPSaZhSSGOsuaQdtfO6haq39+Lq95O3F2jOpTiEUIQdVkW5f+3jap/7CT+7lGdmqGc1TY0ccZecYr3BNHJqexcuYziPTtIzx3F7tXfkjhoMBfe+yDWujo+fOJXANiavaM7e5tglrRJwEohRD6wCdnH8SVwO/CSEGI78EfgDgAhRLIQon0ziHuB9zzjR3vOBfg/IAxYflLY7XQg33PdRcBdkiT1flutLhB+ThphM1K71S7yRGRVC7WfHgAVRF+V3SstKIWnktvQ6KEsvXIp665Zx5IrlvDKua/w/pz3j3f080VVSxVpDidaYN+Sh72Ot1pkc2GNMw2Dqh6jXk/hjirylhSyY1UxTj95L+NmZzDrhiEgwX9e3kJLo4ORszrGSpgjo/jJE3/CHB3N4ldeotUWvGPV0dqI26XtkyqiPYUkuRDizCpm3bbTsB8K3lwluSWq3t2Ns8ZGzPVDfdZv08QYib1hGM4aG7V+qjy46mw9GrnY08z46W1kTzobg9nCjm+XkTp8BJc98jhRicmkDBnG5b9+gmHTZjLtmhtP+dwCfhIlScoHxvh4/TvAy4gtSVIpMKfd823AeB/n+aw5LEnSp8CngeZ1pqCJMYBK4ChpwlHShD4rstccdaPiRnFZ1mXcPuJ2QF6dpVhSSLGk+B3nltyUNZexbvw1nLfmbUZu/YiSsTeTkjb5+DkTxv+cXdowor+w01CWyFcLdnBkV/Xx44U7qpnzsxGo1b7XKjmTkvjmnT20NDiISjKTmuNdZsUUHsHsu+/no6d+w4q3XmX2zx4ITsG69ag0sgOxsXEXzc0HiYgYg9GYHnhsP0Gl0uN2n1ldAtXhOoRRg6M8+MRMt9WBs9xKxIUZGIfHdnqefmAEYeek0bjiCC0j4zAO805cddW3oh8U2ZWpnxISMgdxyQOyZV+SJK/P+sAxExg4ZkJfTE3JHO9rhFqFJsZwvG6PtgfLHpyMVq3l6alPkx4e2g2z0lqJzWUjOmoQ++e+DEDRdx3TdjRqLcNG30JxRSpOSc+RXdVMuSKL2/8ynRnX5nBkVzVfv5JPTRANsUbO7HwXlzo0l8lXzGP3mm/55s1XcNgCN6vSaMNRaV0UFb3Oxk1z2bX7l6z7YSY/rD+PI0feOi3qQJ2JikMIgTbBhKPMf2uB9rR9T4Lx/4Wfk4Y2yUz1+3uxbqvo8H92211Idpff1gf9id6wQHQHRXH0AzRxJlqPyJnZ6vD+l+AVa4xleMxwXs9/nT+WrwFgZMF3Ps+1uWTFlz0xgTHnp6MzaMidnsK0q7MpO1TPJ89tptpHxn17sif4j6mffMU1jLv4cvK/WcI/HrybAxvW+b35azQmhICDBc8TH38h48f/m+zBj6PTxXHg4B85fHi+3/frD5yJigNAm2jGUdYclPJ2VFipemc3qjAdhqH+S9+AXFsu9tZctIkmaj7cR91nB3HW2uTaVA2Be+YodM6ZZTQ9TdHGm7Dtls06moT+V/pbrVLzzux3eHf3u6wolDNdLc5WOLQKMqYDEggVYsWTjDeXU6afxox50ztcY+TMVDJHxfLRMxvJW1zEBbcO73C82RPPbwrXoTX490UIlYpzbriVrAmTWPHWq3z+5z+SNDiHadfeRJqnH3N7nDYnGCEm+hxyh/8FIdREhI8iNfUG9u59jMOFf0OotGQM+Fmfruxs9jIqK5dRXLyQ8PBRDMn5A2qP30mrCcfpbMbtdqBSnZqcg1OBNtGEZHfhqm/ttH9NW65G46qjCJ2K+LtHBeya2YbaoiP+Z6NpWFZE46qjNG8sk5fLnjSP/rhQOx1QFEc/QJt0wjylS+ufCV4GjYE7Rt7BHSPvoGXsZgzvXYl4dy4INejDIDYbijcy8ZybYc5PQO390QqLNjBoXDz7N5ThsLvQtsuu37mmBAT8+OFxQd+8U4cM54Zn57Nr9QrWLXqfj5/6DZmjxzHrlruJ9PQraGlq5MA3NuKy5zDrfllptCGEiiFDnsHttnPo0J9patxDTs6T6HSd2857CoejgcbGHTicDdTW/kBt7Q9YrXLwoNGYQVnZZ7S0HCEn5/eEWYZgMKQCbmy2Ukym4Lsj9ne0noWSo6wZdYSOlh1VCLUKXaoFZ7WNlt3VWLeU47Y6MY6MJeKCjKCVRhtCJYiYnYF+cCTOCivOWjtNa+QCgori6BqK4ugHtG27zRMSe6ywYW9iTB0Pv9gKG98EhxXqiqB8N8x8DKY/DH5u/NnjE9i9tpTCHVUM9pR5cDncXiG4waJSqxkx6wKGnD2DbUu/Yv2nH/DPh3/ORb/4Fcf272Hrki9xOlqZ86OHOyiNNoRQM2zYi5gtORw69DI1tesYnPUoSUlXBKXAHI46qqpXUVe7gejos0lIuMjv+TU131NW/gWVlUuOlxFRq01ERp5FSvI8oqKmYLEMobz8C/btf4qNGy8hJWUe8XGzAbDZis8wxSEvmqrf2SU7s0+q/4ZKYBweg3liIoas0PvStMcwKBIGRSI53Yri6CaK4ugHqHRqkp+cjNCeRi4nYxTM8A7LDUTy4EgsUXp2rComa1w8QggO5pXLIbjndL1cmVanZ8IlP2bIlOn898U/8N8X5Ba0OZOnMeHSK0gY6DOID5B3HhkD7iQu9lz27H2MPXsf4VjZv8nJeQqL2XfSoNvtpLhkIQUFL+B22xFCS+mxj6mqWsGQIc+gVnsHOVRWLmfHzp8fVxSpKdej0YQRFpbrZX5KTLyUmJjpHDo8n+Lid6mv3wZAS8uZ1TmvfQRh46qjGIbFYJmchLOyBXWMAV2KBbWlZ2/u7R3rKr1yC+wKyl+tn6Ay/G/8K4RKMOaCAaz9aD/Fe2pJGxZN/spiohJNpA7t3ooSICwmlsse/i1v338nSYNzuOi+XwVt+jKbsxg39gNKSz/mYMFzbNgwh+Tkq8jM/AV22zEqKpdSXb0atdqM09mA1VpATMw5ZGb+gjDLMAqLXuPw4fk0Ne9j5IhXO4T7VlWvYueu+wmzDGfMmIVBJfNptZHkZD+ByTSQ/fufBMDeeub16k75w1RKHvsegMiLB8qmqMHd/ywEQgRRiFTBN+J0CEUMxPjx46XNm/tVRRIFPzgdLj54agMIwczrh/Dfl7cyfV42I7qx4zgZW1MTWoMBtaZrCrm1tYbCwlcoLnkPSZJLrQihJSpyIi5XM26plYwBPyMu7kcdFFN19Wp27rofl6sFiyUHkykTR2stNbXfYTINZOzYD9B3wYdSV7eZ0tKPSE29gfDw0EqunA607KzC1dCKZUryKXk/R4UVqdWFLrV/+hRPFUKIPEmSvPLsAo5TFIdCX1C8r5b/vrwVAK1BzU3PTkXXD3ddVmsRFRVfoTckExszC602cIXdlpajlJS8T2PjLqwtRbjddtLTbyUt9adnTJFChTODriqO/vdNVfifIDUnimFTk9j9/TEGj0/ol0oDwGQaQEbGz0IaYzSmkZX1SC/NSEGh7+mf31aF/wmmXjkYjU7N2B+dOVFCCgr/CyiKQ6HP0Bk1TLs6u6+noaCgECKnUfyngoKCgkJ/QFEcCgoKCgohoSgOBQUFBYWQUBSHgoKCgkJIKIpDQUFBQSEkFMWhoKCgoBASiuJQUFBQUAgJRXEoKCgoKITEGVGrSghRCRT10uVjgapeuvapRpGl/3GmyAFnjixnihwQWJYBkiTFhXrRM0Jx9CZCiM1dKQLWH1Fk6X+cKXLAmSPLmSIH9J4siqlKQUFBQSEkFMWhoKCgoBASiuIIzBt9PYEeRJGl/3GmyAFnjixnihzQS7IoPg4FBQUFhZBQdhwKCgoKCiGhKA4FBQUFhZBQFAcghPhICLHN81MohNjmef26dq9vE0K4hRCjfYy/Sgixy3O8T8P4ekCWaCHEciHEAc/vqFMtg2cePuXwHBsphPjB8zffIYQw+Bg/ynPODiHEF0KIwM3Ce4kekGW0EGK9Z/xmIcRZp1SAjnPpriydjj+VdFcOz3n3CiH2ec57/pRN3nse3f2fPCmEKGl3jTkB31SSJOWn3Q/wEvC4j9dHAIc6GTMUyAFWAeP7WoZuyvI88GvP418Dz/UnOZC7VuYDozzPYwC1jzGbgBmex7cAT/e1HN2QZRlwoefxHGBVX8vRVVk6G3+6yQHMBL4B9J7n8X0tRzdkeRJ4KJT3UVrHtkMIIYCfALN8HL4G+MDXOEmS9njG997kQqSrsgBzgXM8j/+JrAwf6eHpBY0POS4A8iVJ2g4gSVJ1J0NzgDWex8uBpcDvenGqAemGLBLQtmOKAEp7c57B0A1ZOhvfJ3RDjruBZyVJsnvOq+jtuQaiu/+TUFBMVR2ZBpRLknTAx7Gr6fxm2x/pqiwJkiQdA/D8ju+l+QXLyXJkA5IQYqkQYosQ4ledjNsJXOp5fBWQ1svzDIauynI/8IIQ4ijwIvCb3p9qQLoqS2fj+4quypENTBNCbBBCrBZCTDgls/VPd/4n9wgh8oUQbwdjnv6f2XEIIb4BEn0cekySpP96HvtciQshJgJWSZJ29uIUg+ZMkaWLcmiAs4EJgBVYIYTIkyRpxUnXuAX4qxDiceBzoLVHJ38SvSzL3cADkiR9KoT4CfAWcF6PCtCOXpalDX+73h6hl+XQAFHAJM+5HwshBkoe209P08uyvAo8jbyzfRrZ3HWL3wn1tU2uv/x4/sjlQKqPYy8DjwZxjVX0Ax9Hd2QB9gFJnsdJwL7+JAcwD3in3fPfAQ8HuE42sLG//U+ClQWo50TOlQAaTldZOht/uskBLAHOafe8AIg7HWU56ToZwM5A76eYqk5wHrBXkqTi9i8KIVTIpo4P+2RWXaM7snwO3Oh5fCPwXz/n9ja+5FgKjBRCmIQQGmAGsPvkgUKIeM9vFfBb4LVTMF9/dFkWZJ/GDM/jWUBfm3e6I0tn4/uC7sjxHzy+BCFENqCjbyvqdue7ktTu6eXIZl7/9KXG708/wDvAXT5ePwdY7+P1N/HsLjx/7GLAjqz1l57GssQAK5BvTiuA6H4ox/XALs8H/PlO5LgP2O/5eRbPiv00leVsIA/YDmwAxp2usvgbfzrJgawo/uU5Zwsw6zSWZSGwAzkC63M8Fgd/P0rJEQUFBQWFkFBMVQoKCgoKIaEoDgUFBQWFkFAUh4KCgoJCSCiKQ0FBQUEhJBTFoaCgoKAQEoriUFBQUFAICUVxKCgoKCiExP8DEFgK0D7inQIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9301c018-ac66-4a64-8efe-a839a349e53f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e8d4b939-2d62-4d7a-a866-7283b3e8e383",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  263227.0 people (VAP of 201461.0 ) and  133760.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "84c2cf09-b3ea-4fc2-a55b-62985882836b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n",
      "working on tract 1600\n",
      "working on tract 1800\n",
      "working on tract 2000\n",
      "working on tract 2200\n",
      "working on tract 2400\n",
      "working on tract 2600\n",
      "working on tract 2800\n",
      "working on tract 3000\n",
      "working on tract 3200\n",
      "working on tract 3400\n",
      "working on tract 3600\n",
      "working on tract 3800\n",
      "working on tract 4000\n",
      "working on tract 4200\n",
      "working on tract 4400\n",
      "working on tract 4600\n",
      "working on tract 4800\n",
      "working on tract 5000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rebuild tractMAP, excluding skipped / sliced tracts.  #NOT NEEDED FOR OHIO -- NO SLICED populous TRACTS\n",
    "counter = -1 #Watchout - low-number tracts may have been skipped\n",
    "found = \"no\"\n",
    "while found == \"no\":\n",
    "    counter +=1\n",
    "    if isSkippedTract[counter]==0:\n",
    "        starter = counter\n",
    "        found = \"yes\"\n",
    "\n",
    "tractMAP = tractGeom[starter]\n",
    "for t in range(starter+1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        tractMAP = tractMAP.union(tractGeom[t])\n",
    "\n",
    "if type(tractMAP) != type(tractGeom[1]):\n",
    "    for geom in tractMAP.geoms:\n",
    "        x,y = geom.exterior.xy\n",
    "        plt.plot(x,y)\n",
    "else:\n",
    "    x,y = tractMAP.exterior.xy\n",
    "    plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4538dfc-7d35-46b4-95c1-c5d93169e971",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "650cf37d-e11a-4ed3-861b-3f3edc411422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of precinct's Trump votes by precinct Biden votes?\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"Biden votes in this precinct\", ylabel=\"Trump precinct votes\")\n",
    "plt.plot(vtdBiden, vtdTrump, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "d40408f3-31eb-4b8a-ad91-4e2eb5f422df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.6309834638816362 1149\n",
      "100 0.6832092638544252 1209\n",
      "200 0.12962962962962962 432\n",
      "300 0.3338228095937347 2043\n",
      "400 0.2402938090241343 953\n",
      "500 0.044794952681388014 1585\n",
      "600 0.378234398782344 1314\n",
      "700 0.8970223325062034 806\n",
      "800 0.7370753323485968 3385\n",
      "900 0.9038461538461539 312\n",
      "1000 0.6711864406779661 1180\n",
      "1100 0.15946843853820597 1204\n",
      "1200 0.49886535552193645 2644\n",
      "1300 0.846832397754611 1247\n",
      "1400 0.48575351870923444 5826\n",
      "1500 0.4962302947224126 1459\n",
      "1600 0.6174763214176596 3273\n",
      "1700 0.5126712328767123 2920\n",
      "1800 0.7792642140468228 897\n",
      "1900 0.38082901554404147 2316\n",
      "2000 0.37141200347926934 3449\n",
      "2100 0.46492753623188404 3450\n",
      "2200 0.601963746223565 1324\n",
      "2300 0.5772954924874791 2995\n",
      "2400 0.5558282208588957 1630\n",
      "2500 0.636259977194983 1754\n",
      "2600 0.592255125284738 439\n",
      "2700 0.6048578199052133 5064\n",
      "2800 0.47733160621761656 1544\n",
      "2900 0.7450110864745011 451\n",
      "3000 0.5353241077931536 2746\n",
      "3100 0.5109780439121756 501\n",
      "3200 0.6214592274678111 1165\n",
      "3300 0.5697981906750174 7185\n",
      "3400 0.0 0\n",
      "3500 0.6197466896948762 3474\n",
      "3600 0.6529605263157895 1216\n",
      "3700 0.21333333333333335 150\n",
      "3800 0.6430594900849859 353\n",
      "3900 0.6693584567070435 2229\n",
      "4000 0.14316012725344646 1886\n",
      "4100 0.21107784431137724 668\n",
      "4200 0.4349157733537519 1306\n",
      "4300 0.5617860851505712 1926\n",
      "4400 0.505003335557038 1499\n",
      "4500 0.6038072575847709 1681\n",
      "4600 0.0 0\n",
      "4700 0.33962264150943394 212\n",
      "4800 0.5755395683453237 139\n",
      "4900 0.5774647887323944 71\n",
      "5000 0.5 2\n",
      "5100 0.36153846153846153 1040\n",
      "5200 0.712 125\n",
      "5300 0.36405228758169933 1530\n",
      "5400 0.6463414634146342 82\n",
      "5500 0.42203548085901027 3213\n",
      "5600 0.4521193092621664 637\n",
      "5700 0.531594514654477 3719\n",
      "5800 0.655383507382067 2777\n",
      "5900 0.6666666666666666 738\n",
      "6000 0.4823529411764706 595\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ALL TRIMS COMPLETE. let's convert the partisan data to the format we use for HD code\n",
    "# in this prototype, we only use 2020 presidential data, but this could be modified\n",
    "vtdGOP = [0.]*nPrecincts  #Each precinct's R/(R+D)\n",
    "vtdPop = [0.]*nPrecincts   #THIS IS VOTER POPULATION\n",
    "vtdArea = [0.]*nPrecincts\n",
    "vtdCPx = [0.]*nPrecincts   #these are centroid x and y of each precinct\n",
    "vtdCPy = [0.]*nPrecincts\n",
    "plotcolor = ['blue']*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    vtdPop[p] = vtdTrump[p] + vtdBiden[p] \n",
    "    vtdGOP[p] = vtdTrump[p]/max((vtdTrump[p] + vtdBiden[p]), 0.01)  #avoid divide by zero\n",
    "    if (vtdGOP[p] > 0.40) :\n",
    "        plotcolor[p]='purple'\n",
    "        if (vtdGOP[p] > 0.60) :\n",
    "            plotcolor[p] = 'red'\n",
    "    vtdArea[p] = vtdGeom[p].area\n",
    "    vtdCPx[p] = vtdGeom[p].centroid.x  #not needed except for graphing\n",
    "    vtdCPy[p] = vtdGeom[p].centroid.y   #not needed  \"  \"  \"\n",
    "    if isSkippedPrecinct[p] == 0 and p%10 == 0: #memory drain if we print all\n",
    "        plt.scatter(vtdCPx[p], vtdCPy[p], marker='.',c=plotcolor[p])\n",
    "for p in range (nPrecincts):\n",
    "    if p%100 == 0:\n",
    "        print(p,vtdGOP[p],vtdPop[p])\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "a1e3f0d2-c21c-46d4-8227-e75dfd5e89b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original and final map areas are 17.04014292887745 14.675697782397593\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MAP = tractMAP  #committing to the trimmed map (for TN, using convex hull)\n",
    "#bufferDistance = 0.02  #do minor buffer for OH\n",
    "origMAP = wholeMAP    # let's keep a copy of the original map prior to buffering\n",
    "#MAPbuffer = MAP.exterior.buffer(bufferDistance, single_sided=True)  #gives a little exterior buffer\n",
    "# MAP = origMAP.convex_hull  #alternate to buffer for weird state shapes\n",
    "bBox = MAP.bounds\n",
    "MAPMinX = bBox[0]  #these four are useful for slicing ops, and are basis for map grid (without buffer)\n",
    "MAPMinY = bBox[1]\n",
    "MAPMaxX = bBox[2]\n",
    "MAPMaxY = bBox[3]\n",
    "\n",
    "#MAP = MAP.union(MAPbuffer)  #not buffering for TN\n",
    "print(\"original and final map areas are\",origMAP.area,MAP.area)  #same if we didn't buffer\n",
    "x2, y2 = MAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"green\")\n",
    "if origMAP.type == tractGeom[0].type :  #single Polygon\n",
    "    x2, y2 = origMAP.exterior.xy\n",
    "    plt.plot(x2,y2,c=\"blue\")\n",
    "else:\n",
    "    for geom in origMAP.geoms:\n",
    "        x2,y2 = geom.exterior.xy\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "plt.show()\n",
    "\n",
    "minTractPop = 10  #was zero to not exclude empty interiors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "8e271f55-e118-4575-bc99-c28c59f435b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020 population, Trump+Biden voters, lean =  20768925 10551447 0.5169475466214156\n",
      "compare to Census 21538187 Leip has Trump + Biden = 10965776.0 0.5169475466214156\n"
     ]
    }
   ],
   "source": [
    "#let's confirm some population stats - WILL BE OFF FOR FL WITH OUR SLICE-OUT\n",
    "censusPop = 21538187\n",
    "atlasTrump = 5668731.\n",
    "atlasBiden = 5297045.\n",
    "\n",
    "print(\"2020 population, Trump+Biden voters, lean = \",np.sum(tractPop),np.sum(vtdPop),stateGOP )\n",
    "print(\"compare to Census\",censusPop,\"Leip has Trump + Biden =\",atlasTrump+atlasBiden,\n",
    "      atlasTrump/(atlasTrump+atlasBiden) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "6c47f717-f208-4a4c-b825-a3181a83c6ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state pop before, after slice operation are 20768925 20768925.0\n",
      "state Trumpers before, after slice opn are 5393593 5393593.0\n",
      "state Bideners before, after slice opn are 5157854 5157854.0\n"
     ]
    }
   ],
   "source": [
    "#Check on integrity after slicing\n",
    "sumPop = 0.\n",
    "sumTrump = 0.\n",
    "sumBiden = 0.\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] == 0:\n",
    "        sumPop += tractPop[t]\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] == 0:\n",
    "        sumTrump += vtdTrump[p]\n",
    "        sumBiden += vtdBiden[p]\n",
    "print(\"state pop before, after slice operation are\",np.sum(tractPop),sumPop)\n",
    "print(\"state Trumpers before, after slice opn are\",np.sum(vtdTrump),sumTrump)\n",
    "print(\"state Bideners before, after slice opn are\",np.sum(vtdBiden),sumBiden)\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] ==1 and tractPop[t] > 0:  #should not occur\n",
    "        print(\"missed pop for tract, pop, (x,y)\",t,tractPop[t],tractGeom[t].centroid.x,\n",
    "              tractGeom[t].centroid.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "b5334311-b597-4568-88c7-eb3c9d4bdf09",
   "metadata": {},
   "outputs": [],
   "source": [
    "#FAIL here with a bad precinct when building grid map in Georgia.  See below two blocks for fix and re-run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3eb2c10-5caa-42ce-a70f-d87d6e275e22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2034 1\n",
      "0 0.00016990724566590647\n",
      "1 2.9354445031118904e-11\n",
      "2 4.989851763673341e-17\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#WTF is going on with this precinct?\n",
    "print(p, notPolyVTD[p])\n",
    "counter = 0\n",
    "for geom in vtdGeom[p].geoms:\n",
    "    x,y = geom.exterior.xy\n",
    "    plt.plot(x,y)\n",
    "    print(counter,geom.area)\n",
    "    counter+=1\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2532d156-de55-4a64-ac38-1a373a9ab910",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#OK, kill the bad sub-polygons\n",
    "counter = 0\n",
    "for geom in vtdGeom[p].geoms:\n",
    "    if counter == 0:\n",
    "        goodGeom = geom\n",
    "    counter+=1\n",
    "vtdGeom[p] = goodGeom\n",
    "notPolyVTD[p] = 0\n",
    "x,y = vtdGeom[p].exterior.xy\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "2f6c72cd-90d8-489d-9b15-d4278ba17b3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map has a total of 210 grids for 27 districts\n",
      "starting grid no 0 at x = -86.18546219999999, y = 25.41791764285714 \n",
      "starting grid no 5 at x = -86.18546219999999, y = 27.485714071428575 \n",
      "starting grid no 10 at x = -86.18546219999999, y = 29.5535105 \n",
      "starting grid no 15 at x = -85.7571066, y = 25.831476928571426 \n",
      "starting grid no 20 at x = -85.7571066, y = 27.899273357142853 \n",
      "starting grid no 25 at x = -85.7571066, y = 29.967069785714283 \n",
      "starting grid no 30 at x = -85.328751, y = 26.24503621428571 \n",
      "starting grid no 35 at x = -85.328751, y = 28.312832642857146 \n",
      "starting grid no 40 at x = -85.328751, y = 30.38062907142857 \n",
      "starting grid no 45 at x = -84.9003954, y = 26.6585955 \n",
      "starting grid no 50 at x = -84.9003954, y = 28.726391928571427 \n",
      "starting grid no 55 at x = -84.9003954, y = 30.794188357142858 \n",
      "starting grid no 60 at x = -84.47203979999998, y = 27.072154785714282 \n",
      "starting grid no 65 at x = -84.47203979999999, y = 29.139951214285716 \n",
      "starting grid no 70 at x = -84.04368419999999, y = 25.417917642857137 \n",
      "starting grid no 75 at x = -84.04368419999999, y = 27.485714071428568 \n",
      "starting grid no 80 at x = -84.04368419999999, y = 29.5535105 \n",
      "starting grid no 85 at x = -83.61532859999998, y = 25.831476928571423 \n",
      "starting grid no 90 at x = -83.61532859999998, y = 27.899273357142853 \n",
      "starting grid no 95 at x = -83.61532859999998, y = 29.967069785714287 \n",
      "starting grid no 100 at x = -83.186973, y = 26.24503621428571 \n",
      "starting grid no 105 at x = -83.186973, y = 28.312832642857146 \n",
      "starting grid no 110 at x = -83.186973, y = 30.38062907142857 \n",
      "starting grid no 115 at x = -82.75861739999999, y = 26.6585955 \n",
      "starting grid no 120 at x = -82.7586174, y = 28.726391928571427 \n",
      "starting grid no 125 at x = -82.7586174, y = 30.794188357142858 \n",
      "starting grid no 130 at x = -82.33026179999999, y = 27.072154785714286 \n",
      "starting grid no 135 at x = -82.3302618, y = 29.139951214285716 \n",
      "starting grid no 140 at x = -81.90190620000001, y = 25.41791764285714 \n",
      "starting grid no 145 at x = -81.90190620000001, y = 27.485714071428575 \n",
      "starting grid no 150 at x = -81.90190620000001, y = 29.5535105 \n",
      "starting grid no 155 at x = -81.4735506, y = 25.831476928571423 \n",
      "starting grid no 160 at x = -81.4735506, y = 27.899273357142853 \n",
      "starting grid no 165 at x = -81.4735506, y = 29.967069785714287 \n",
      "starting grid no 170 at x = -81.04519499999999, y = 26.24503621428571 \n",
      "starting grid no 175 at x = -81.04519499999999, y = 28.312832642857146 \n",
      "starting grid no 180 at x = -81.04519499999999, y = 30.38062907142857 \n",
      "starting grid no 185 at x = -80.6168394, y = 26.658595499999997 \n",
      "starting grid no 190 at x = -80.6168394, y = 28.726391928571424 \n",
      "starting grid no 195 at x = -80.6168394, y = 30.794188357142858 \n",
      "starting grid no 200 at x = -80.1884838, y = 27.072154785714282 \n",
      "starting grid no 205 at x = -80.1884838, y = 29.139951214285716 \n",
      "done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids = 1116561.1003651982 15439991.381528206 105.0\n"
     ]
    }
   ],
   "source": [
    "# this section - ESTIMATE POP'N DENSITY at scale of 1/3 of district length\n",
    "# also, IDENTIFY WHICH TRACTS and VTD's INTERSECT EACH GRID to speed later intersection searches by grid \n",
    "# (above sections already pulled in Tract and Precinct geometry and demographic data, built an overall map)\n",
    "nDistricts = 27   #Florida - panhandle\n",
    "avgDistrictPop = np.sum(tractPop) / float(nDistricts)\n",
    "#MAPMaxX = -80.   #In most state maps, we figure this out from the bounding box of the convex hull; see above.\n",
    "#MAPMaxY = 31.1   # ... but for Florida, we will do manually\n",
    "#MAPMinX = -87.0\n",
    "#MAPMinY = 25.5\n",
    "stateWidth = float(MAPMaxX - MAPMinX)\n",
    "stateHeight = float(MAPMaxY - MAPMinY)\n",
    "stateWHRatio = stateWidth / stateHeight\n",
    "G = 3  #adjustable parameter; G*G = number of grids that fit in an average district\n",
    "nGridsX = int( G*round((nDistricts*stateWHRatio)**0.5,0) )   #OK to have empty grids for a non-rectglr state\n",
    "nGridsY = int(round(nGridsX / stateWHRatio,0) )  #so grids are square even if state has high aspect ratio\n",
    "nGrids = int(nGridsX*nGridsY)\n",
    "print(\"map has a total of {0} grids for {1} districts\".format(nGrids,nDistricts) )\n",
    "nGridPrecincts = [0]*nGrids # will store how many precincts intersect with each grid square\n",
    "nGridTracts = [0]*nGrids    # same, but for tracts\n",
    "gridPrecinctNo = [[0]*nPrecincts for gridNo in range(nGrids) ] # initialize a list of VTDs that intersect with each grid square\n",
    "gridTractNo = [[0]*nTracts for gridNo in range(nGrids) ] # initialize a list of tracts that intersect with each grid square\n",
    "\n",
    "gridPop = [0.]*nGrids #will store approx total population for this grid square\n",
    "gridDensity = [0.]*nGrids\n",
    "gridGeom = [Polygon([(0,0),(0,1),(1,0)])]*nGrids  #initialize grid geometry\n",
    "gridWidth = stateWidth /nGridsX        #geo length of a grid's side length\n",
    "gridHeight = stateHeight /nGridsY    \n",
    "\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG/nGridsY)\n",
    "    y = int(nG % nGridsY)\n",
    "    point1 = Point(x*gridWidth+MAPMinX,y*gridHeight+MAPMinY)\n",
    "    point2 = Point((x+1)*gridWidth+MAPMinX, y*gridHeight+MAPMinY)\n",
    "    point3 = Point((x+1)*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    point4 = Point(x*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    gridGeom[nG] = Polygon([point1, point2, point3, point4])\n",
    "    if (nG %5 == 0): #print occasionally, should take about one second per grid\n",
    "        print(\"starting grid no {0} at x = {1}, y = {2} \".format(nG,gridGeom[nG].centroid.x,gridGeom[nG].centroid.y) )\n",
    "    counter = 0\n",
    "    #    Now for each grid square, find intersxn with all polygons, total the intersection population\n",
    "    #    Also, create 2 lists: of TRACTS and precincts that intersect each grid (efficiency shortcut)\n",
    "    \n",
    "    if( gridGeom[nG].intersection(MAP).area > 0.0001) : #don't bother w grids off the map\n",
    "        for t in range(nTracts):\n",
    "            # if (t%3000 == 0) :\n",
    "            #    print (\"grid {0}, tract no {1}, ({2},{3})\".format(nG,t,tractGeom[t].centroid.x,tractGeom[t].centroid.y) )\n",
    "            intersxnArea = 0.\n",
    "            if (notPoly[t] == 0) :  # tract is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(tractGeom[t]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #tract is a multiPolygon, do the polygon intersections individually\n",
    "                #print(\"I think tract\",t,\"is a multiPolygon\")\n",
    "                for geom in tractGeom[t].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            if (intersxnArea > 0 and tractPop[t] > minTractPop) : # this tract is at least partially in this grid and populated \n",
    "                if (tractArea[t] == 0):  #should not happen, but debugging\n",
    "                    print(t,tractGeom[t].centroid.x, tractGeom[t].centroid.y)  #debug print\n",
    "                gridPop[nG] += tractPop[t]*intersxnArea/tractArea[t]\n",
    "                if tractPop[t] > (1.0 * avgDistrictPop) :  #flag if we have a mega tract\n",
    "                    uhoh = input(\"Uh oh! We have a tract that's bigger than a district.  What now?\")\n",
    "                else :\n",
    "                    gridTractNo[nG][counter] = t  #add this tract to this grid's list, update the no of tracts in grid\n",
    "                    counter +=1            \n",
    "                    nGridTracts[nG] = counter\n",
    "        # OK, now, loop over PRECINCTS to develop each grid's list of precincts (prev loop was for tracts)\n",
    "        counter = 0\n",
    "        for p in range(nPrecincts):\n",
    "            intersxnArea = 0.\n",
    "            if (notPolyVTD[p] == 0) :  # precinct is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #precinct is a multiPolygon, do the polygon intersections individually\n",
    "                for geom in vtdGeom[p].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area\n",
    "            if (intersxnArea > 0 and vtdPop[p] >0) : # this precinct is at least partially in this grid and recorded votes                 \n",
    "                gridPrecinctNo[nG][counter] = p  #add this precinct to this grid's list, update the no of tracts in grid\n",
    "                counter +=1            \n",
    "                nGridPrecincts[nG] = counter\n",
    "    gridDensity[nG] = gridPop[nG] / gridGeom[nG].area  #finished loop over tracts & vtd's.  Calceach grid's population density    \n",
    "\n",
    "minGridPop = np.min(gridPop)\n",
    "# avgGridPop = np.mean(gridPop)   #see below for explicit averaging, since there are so many empty grids\n",
    "maxGridPop = np.max(gridPop)\n",
    "nPopulatedGrids = 0.\n",
    "totPopulatedGridArea = 0.\n",
    "for nG in range(nGrids) :\n",
    "    if gridPop[nG] > 0. :\n",
    "        nPopulatedGrids +=1\n",
    "        totPopulatedGridArea += gridGeom[nG].area\n",
    "avgGridPop = np.sum(tractPop) / nPopulatedGrids\n",
    "avgGridDensity = np.sum(tractPop) / totPopulatedGridArea\n",
    "#avgGridDensity = np.average(gridDensity) #avgGridPop / (gridPL * gridPL) #these densities help home district algorithm\n",
    "maxGridDensity = np.max(gridDensity)\n",
    "print(\"done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids =\",avgGridDensity,maxGridDensity,nPopulatedGrids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "c86e79b4-f9c9-4de1-9b8a-374fc9761f02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grid 0 is empty\n",
      "grid 1 is empty\n",
      "grid 2 is empty\n",
      "grid 3 is empty\n",
      "grid 4 is empty\n",
      "grid 5 is empty\n",
      "grid 6 is empty\n",
      "grid 7 is empty\n",
      "grid 8 is empty\n",
      "grid 9 is empty\n",
      "grid 10 is empty\n",
      "grid 11 is empty\n",
      "grid 14 is empty\n",
      "grid 15 is empty\n",
      "grid 16 is empty\n",
      "grid 17 is empty\n",
      "grid 18 is empty\n",
      "grid 19 is empty\n",
      "grid 20 is empty\n",
      "grid 21 is empty\n",
      "grid 22 is empty\n",
      "grid 23 is empty\n",
      "grid 24 is empty\n",
      "grid 28 is empty\n",
      "grid 29 is empty\n",
      "grid 30 is empty\n",
      "grid 31 is empty\n",
      "grid 32 is empty\n",
      "grid 33 is empty\n",
      "grid 34 is empty\n",
      "grid 35 is empty\n",
      "grid 36 is empty\n",
      "grid 37 is empty\n",
      "grid 42 is empty\n",
      "grid 43 is empty\n",
      "grid 44 is empty\n",
      "grid 45 is empty\n",
      "grid 46 is empty\n",
      "grid 47 is empty\n",
      "grid 48 is empty\n",
      "grid 49 is empty\n",
      "grid 50 is empty\n",
      "grid 51 is empty\n",
      "grid 56 is empty\n",
      "grid 57 is empty\n",
      "grid 58 is empty\n",
      "grid 59 is empty\n",
      "grid 60 is empty\n",
      "grid 61 is empty\n",
      "grid 62 is empty\n",
      "grid 63 is empty\n",
      "grid 64 is empty\n",
      "grid 65 is empty\n",
      "grid 70 is empty\n",
      "grid 71 is empty\n",
      "grid 72 is empty\n",
      "grid 73 is empty\n",
      "grid 74 is empty\n",
      "grid 75 is empty\n",
      "grid 76 is empty\n",
      "grid 77 is empty\n",
      "grid 78 is empty\n",
      "grid 79 is empty\n",
      "grid 80 is empty\n",
      "grid 84 is empty\n",
      "grid 85 is empty\n",
      "grid 86 is empty\n",
      "grid 87 is empty\n",
      "grid 88 is empty\n",
      "grid 89 is empty\n",
      "grid 90 is empty\n",
      "grid 91 is empty\n",
      "grid 92 is empty\n",
      "grid 93 is empty\n",
      "grid 98 is empty\n",
      "grid 99 is empty\n",
      "grid 100 is empty\n",
      "grid 101 is empty\n",
      "grid 102 is empty\n",
      "grid 103 is empty\n",
      "grid 104 is empty\n",
      "grid 105 is empty\n",
      "grid 106 is empty\n",
      "grid 112 is empty\n",
      "grid 113 is empty\n",
      "grid 114 is empty\n",
      "grid 115 is empty\n",
      "grid 126 is empty\n",
      "grid 127 is empty\n",
      "grid 139 is empty\n",
      "grid 140 is empty\n",
      "grid 154 is empty\n",
      "grid 180 is empty\n",
      "grid 181 is empty\n",
      "grid 192 is empty\n",
      "grid 193 is empty\n",
      "grid 194 is empty\n",
      "grid 195 is empty\n",
      "grid 203 is empty\n",
      "grid 204 is empty\n",
      "grid 205 is empty\n",
      "grid 206 is empty\n",
      "grid 207 is empty\n",
      "grid 208 is empty\n",
      "grid 209 is empty\n"
     ]
    }
   ],
   "source": [
    "isEmptyGrid = [0]*nGrids\n",
    "for nG in range(nGrids):\n",
    "    if gridPop[nG] == 0:\n",
    "        isEmptyGrid[nG] = 1\n",
    "        print(\"grid\",nG,\"is empty\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "808f513f-21af-4b03-8061-fdeeed33dafd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a heat map of grid density \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here is a heat map of grid density \")\n",
    "xyGridDensity = [[0.]*nGridsX for x in range (nGridsY) ]   #flipping x and y to get right orientation in pixel grid\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG % nGridsY)\n",
    "    y = int(nG / nGridsY)\n",
    "    if gridDensity[nG] > 0 :  #avoid log zero\n",
    "        xyGridDensity[x][y]=np.log(gridDensity[nG])\n",
    "c=plt.pcolormesh(xyGridDensity)\n",
    "plt.colorbar(c)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4013f80b-4fc8-40a1-b708-ee29c29b3ffe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.5461515915376092e-06 8.813567499988873e-06\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(np.min(vtdArea), np.min(tractArea))\n",
    "minTractPop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "ab450500-f1a4-4292-a2fe-71eb2217121d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am working on tract number 0 of 5160 tracts\n",
      "I am working on tract number 20 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 22 6.0 0 115.7 0.1906 116532.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 24 6.0 0 90.0 0.3779 167908.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 28 5.0 0 120.9 0.2324 111058.3\n",
      "I am working on tract number 40 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 50 3.0 0 90.0 0.8725 44573.2\n",
      "I am working on tract number 60 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 65 7.0 0 83.5 0.2734 219334.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 65 8.0 0 83.5 0.2473 219334.3\n",
      "we have 2 non-opposing shorted wedges for tract no 71\n",
      "we have 2 non-opposing shorted wedges for tract no 72\n",
      "I am working on tract number 80 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 95 6.0 0 141.7 0.5429 24035.0\n",
      "I am working on tract number 100 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 104 3.0 2 90.0 0.3456 99030.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 104 4.0 2 90.0 0.5497 99030.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 104 4.0 3 90.0 1.5065 64725.8\n",
      "we have 2 non-opposing shorted wedges for tract no 104\n",
      "I am working on tract number 120 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 128 2.0 3 90.0 0.5327 12225.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 139 4.0 3 90.0 0.3176 122305.1\n",
      "I am working on tract number 140 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 145 2.0 3 90.0 0.6962 17632.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 147 2.0 1 90.0 0.701 27257.0\n",
      "I am working on tract number 160 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 165 4.0 1 90.0 0.1949 198101.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 165 5.0 1 90.0 0.1906 198101.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 165 6.0 1 90.0 0.1864 198101.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 165 7.0 1 90.0 0.1823 198101.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 165 8.0 1 90.0 0.1783 198101.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 165 9.0 1 90.0 0.1743 198101.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 165 10.0 1 90.0 0.1705 198101.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 165 11.0 1 90.0 0.1667 198101.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 165 12.0 1 90.0 0.163 198101.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 165 13.0 1 90.0 0.1594 198101.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 165 14.0 1 90.0 0.1559 198101.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 165 15.0 1 90.0 0.1524 198101.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 165 16.0 1 90.0 0.1491 198101.1\n",
      "I am working on tract number 180 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 193 4.0 3 90.0 0.4853 158508.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 194 5.0 0 143.8 1.7176 24331.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 198 4.0 3 90.0 0.6831 152331.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 198 5.0 3 90.0 0.8098 152331.9\n",
      "I am working on tract number 200 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 203 2.0 2 90.0 0.1041 56036.6\n",
      "we have 2 non-opposing shorted wedges for tract no 203\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 213 9.0 2 90.0 0.8564 186961.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 213 10.0 2 90.0 0.8747 186961.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 213 11.0 2 90.0 0.8933 186961.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 218 5.0 0 139.8 0.5606 34670.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 218 6.0 0 139.8 1.2979 34670.1\n",
      "I am working on tract number 220 of 5160 tracts\n",
      "I am working on tract number 240 of 5160 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 250 3 392221.468314991 1.7833\n",
      "8 yoyos for tract,wedge,wedgePop,r= 250 3 90991.62647833274 0.8916\n",
      "9 yoyos for tract,wedge,wedgePop,r= 250 3 401955.56102171174 1.8332\n",
      "9 yoyos for tract,wedge,wedgePop,r= 250 3 197724.34287589343 1.3624\n",
      "10 yoyos for tract,wedge,wedgePop,r= 250 3 104575.60873800184 1.127\n",
      "10 yoyos for tract,wedge,wedgePop,r= 250 3 112373.23986709242 1.2447\n",
      "10 yoyos for tract,wedge,wedgePop,r= 250 3 121670.41408245731 1.3036\n",
      "10 yoyos for tract,wedge,wedgePop,r= 250 3 146915.3656974394 1.333\n",
      "10 yoyos for tract,wedge,wedgePop,r= 250 3 171869.49393163485 1.3477\n",
      "10 yoyos for tract,wedge,wedgePop,r= 250 3 185496.03682110796 1.3551\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 251 2.0 0 90.0 0.6064 39491.4\n",
      "I am working on tract number 260 of 5160 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 267\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 275 4.0 0 142.9 0.7703 7353.9\n",
      "I am working on tract number 280 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 284 3.0 3 90.0 0.277 90270.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 285 3.0 3 90.0 0.2166 111223.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 285 4.0 3 90.0 0.319 111223.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 286 2.0 1 90.0 0.3347 13029.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 288 5.0 0 90.0 0.2475 167366.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 288 6.0 0 90.0 0.2742 167366.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 288 7.0 0 90.0 0.3038 167366.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 288 8.0 0 90.0 0.3365 167366.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 288 9.0 0 90.0 0.3728 167366.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 288 10.0 0 90.0 0.4131 167366.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 289 3.0 2 90.0 0.3966 85223.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 295 9.0 0 90.0 0.5993 188202.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 295 10.0 0 90.0 0.6091 188202.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 295 11.0 0 90.0 0.619 188202.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 295 12.0 0 90.0 0.629 188202.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 295 13.0 0 90.0 0.6393 188202.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 295 14.0 0 90.0 0.6497 188202.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 295 15.0 0 90.0 0.6602 188202.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 295 16.0 0 90.0 0.671 188202.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 295 17.0 0 90.0 0.6819 188202.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 295 18.0 0 90.0 0.693 188202.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 295 19.0 0 90.0 0.7042 188202.3\n",
      "loop31.0, tr295,wedgePops768426.38, 189038.5, 193152.8, 193112.7, 193122.3, Overedge?1, 0, 0, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 193393.6,0.4215, 193393.6,0.0005, 193393.6,0.0004, 193393.6,0.0011\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 296 4.0 1 90.0 0.2349 142591.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 296 5.0 1 90.0 0.2917 142591.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 296 6.0 1 90.0 0.3623 142591.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 296 7.0 1 90.0 0.4499 142591.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 296 12.0 0 83.7 0.3211 126727.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 297 4.0 2 90.0 0.3001 137151.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 297 5.0 2 90.0 0.3829 137151.6\n",
      "I am working on tract number 300 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 306 7.0 0 133.8 1.1838 83564.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 307 2.0 0 90.0 0.1933 51923.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 307 5.0 0 97.4 0.2161 40831.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 308 2.0 2 90.0 0.2035 95305.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 308 3.0 2 90.0 0.3319 95305.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 310 7.0 0 107.7 0.4814 113129.0\n",
      "7 yoyos for tract,wedge,wedgePop,r= 312 3 298697.82777990756 0.5682\n",
      "8 yoyos for tract,wedge,wedgePop,r= 312 3 158032.41775599812 0.2841\n",
      "9 yoyos for tract,wedge,wedgePop,r= 312 3 298710.9812250009 0.5683\n",
      "9 yoyos for tract,wedge,wedgePop,r= 312 3 256816.9966194639 0.4262\n",
      "10 yoyos for tract,wedge,wedgePop,r= 312 3 179456.70123067725 0.3552\n",
      "11 yoyos for tract,wedge,wedgePop,r= 312 3 221527.55378964802 0.3907\n",
      "11 yoyos for tract,wedge,wedgePop,r= 312 3 198283.12524671055 0.3729\n",
      "12 yoyos for tract,wedge,wedgePop,r= 312 3 188344.65381052485 0.364\n",
      "we have 2 non-opposing shorted wedges for tract no 315\n",
      "I am working on tract number 320 of 5160 tracts\n",
      "we have 2 OPPOSING shorted wedges for tract no 327\n",
      "I am working on tract number 340 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 344 4.0 1 90.0 0.6607 190757.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 344 5.0 1 90.0 0.6647 190757.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 344 6.0 1 90.0 0.6687 190757.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 344 7.0 1 90.0 0.6728 190757.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 344 8.0 1 90.0 0.6768 190757.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 345 5.0 0 137.7 0.6111 91450.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 347 6.0 0 139.8 0.5442 146160.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 347 7.0 0 139.8 0.6642 146160.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 347 8.0 0 139.8 0.8107 146160.2\n",
      "I am working on tract number 360 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 376 2.0 0 90.0 0.4466 8868.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 376 5.0 0 140.9 0.4358 15218.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 377 2.0 0 90.0 0.3292 16846.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 378 2.0 3 90.0 0.3167 26121.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 378 5.0 0 90.4 0.3157 26297.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 379 4.0 2 90.0 0.2283 156938.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 379 5.0 2 90.0 0.2649 156938.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 379 6.0 2 90.0 0.3074 156938.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 379 7.0 2 90.0 0.3567 156938.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 379 8.0 2 90.0 0.414 156938.4\n",
      "I am working on tract number 380 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 383 9.0 0 134.9 0.369 266046.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 383 10.0 0 134.9 0.3443 266046.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 384 5.0 0 99.2 0.3495 252123.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 384 6.0 0 99.2 0.3452 252123.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 384 7.0 0 99.2 0.3409 252123.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 384 8.0 0 99.2 0.3366 252123.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 384 9.0 0 99.2 0.3325 252123.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 384 10.0 0 99.2 0.3283 252123.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 384 11.0 0 99.2 0.3243 252123.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 384 12.0 0 99.2 0.3202 252123.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 384 13.0 0 99.2 0.3163 252123.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 384 14.0 0 99.2 0.3123 252123.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 384 15.0 0 99.2 0.3085 252123.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 384 16.0 0 99.2 0.3046 252123.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 384 17.0 0 99.2 0.3008 252123.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 384 18.0 0 99.2 0.2971 252123.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 384 19.0 0 99.2 0.2934 252123.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 384 20.0 0 99.2 0.2898 252123.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 384 21.0 0 99.2 0.2862 252123.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 384 22.0 0 99.2 0.2826 252123.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 384 23.0 0 99.2 0.2791 252123.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 384 24.0 0 99.2 0.2756 252123.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 384 25.0 0 99.2 0.2722 252123.1\n",
      "I am working on tract number 400 of 5160 tracts\n",
      "I am working on tract number 420 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 433 7.0 0 87.0 0.34 80354.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 437 4.0 3 36.3 0.4555 47697.4\n",
      "I am working on tract number 440 of 5160 tracts\n",
      "I am working on tract number 460 of 5160 tracts\n",
      "I am working on tract number 480 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 486 10.0 3 36.3 1.3056 233521.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 486 11.0 3 36.3 1.386 233521.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 498 5.0 0 90.0 1.948 183019.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 498 6.0 0 90.0 2.0212 183019.9\n",
      "I am working on tract number 500 of 5160 tracts\n",
      "I am working on tract number 520 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 527 6.0 0 119.7 0.1516 135042.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 527 7.0 0 119.7 0.1956 135042.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 538 9.0 0 99.4 0.2635 220541.9\n",
      "I am working on tract number 540 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 558 8.0 0 98.3 0.3691 199279.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 558 9.0 0 98.3 0.3593 199279.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 558 10.0 0 98.3 0.3498 199279.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 558 11.0 0 98.3 0.3405 199279.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 558 12.0 0 98.3 0.3315 199279.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 558 13.0 0 98.3 0.3227 199279.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 558 14.0 0 98.3 0.3142 199279.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 558 15.0 0 98.3 0.3058 199279.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 558 16.0 0 98.3 0.2977 199279.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 558 17.0 0 98.3 0.2899 199279.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 558 18.0 0 98.3 0.2822 199279.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 558 19.0 0 98.3 0.2747 199279.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 558 20.0 0 98.3 0.2674 199279.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 558 21.0 0 98.3 0.2603 199279.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 558 22.0 0 98.3 0.2535 199279.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 558 23.0 0 98.3 0.2467 199279.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 558 24.0 0 98.3 0.2402 199279.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 558 25.0 0 98.3 0.2338 199279.9\n",
      "I am working on tract number 560 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 571 5.0 0 135.1 0.1643 55269.3\n",
      "I am working on tract number 580 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 581 4.0 0 136.9 0.1502 48439.2\n",
      "we have 2 non-opposing shorted wedges for tract no 582\n",
      "I am working on tract number 600 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 608 5.0 3 64.3 0.1655 187262.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 608 6.0 3 64.3 0.1688 187262.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 608 7.0 3 64.3 0.1722 187262.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 608 8.0 3 64.3 0.1756 187262.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 608 9.0 3 64.3 0.1836 187262.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 608 10.0 3 64.3 0.1919 187262.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 608 11.0 3 64.3 0.2006 187262.7\n",
      "I am working on tract number 620 of 5160 tracts\n",
      "I am working on tract number 640 of 5160 tracts\n",
      "I am working on tract number 660 of 5160 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 669\n",
      "we have 2 non-opposing shorted wedges for tract no 672\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 677 3.0 0 122.2 0.1914 95847.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 678 3.0 0 137.7 0.2132 88758.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 678 4.0 0 137.7 0.3642 88758.8\n",
      "I am working on tract number 680 of 5160 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 687 1 300005.61118118546 0.668\n",
      "8 yoyos for tract,wedge,wedgePop,r= 687 1 54889.10504911086 0.334\n",
      "9 yoyos for tract,wedge,wedgePop,r= 687 1 299573.4596317108 0.6562\n",
      "10 yoyos for tract,wedge,wedgePop,r= 687 1 155234.7245064431 0.4951\n",
      "11 yoyos for tract,wedge,wedgePop,r= 687 1 283704.3357699467 0.5756\n",
      "11 yoyos for tract,wedge,wedgePop,r= 687 1 225732.5890583943 0.5354\n",
      "I am working on tract number 700 of 5160 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 710 1 328857.12352360255 1.7494\n",
      "8 yoyos for tract,wedge,wedgePop,r= 710 1 18402.334923431394 0.8747\n",
      "9 yoyos for tract,wedge,wedgePop,r= 710 1 406683.2190507658 2.5008\n",
      "9 yoyos for tract,wedge,wedgePop,r= 710 1 283366.79913712566 1.6877\n",
      "10 yoyos for tract,wedge,wedgePop,r= 710 1 23948.473719585483 1.2812\n",
      "10 yoyos for tract,wedge,wedgePop,r= 710 1 56906.95160293071 1.4845\n",
      "10 yoyos for tract,wedge,wedgePop,r= 710 1 145241.29394321854 1.5861\n",
      "11 yoyos for tract,wedge,wedgePop,r= 710 1 205302.84007988125 1.6369\n",
      "12 yoyos for tract,wedge,wedgePop,r= 710 1 173526.32363413408 1.6115\n",
      "12 yoyos for tract,wedge,wedgePop,r= 710 1 187906.03938353845 1.6242\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 716 7.0 0 125.0 0.1777 122854.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 716 8.0 0 125.0 0.2446 122854.9\n",
      "I am working on tract number 720 of 5160 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 731\n",
      "I am working on tract number 740 of 5160 tracts\n",
      "I am working on tract number 760 of 5160 tracts\n",
      "I am working on tract number 780 of 5160 tracts\n",
      "I am working on tract number 800 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 805 5.0 1 88.5 0.2547 6735.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 814 3.0 0 90.0 0.1546 119899.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 816 7.0 0 105.0 0.1589 139254.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 816 8.0 0 105.0 0.2007 139254.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 819 4.0 0 142.4 0.2371 37898.9\n",
      "I am working on tract number 820 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 827 7.0 0 139.0 0.2336 30844.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 839 6.0 0 123.0 0.2459 94480.0\n",
      "I am working on tract number 840 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 844 5.0 0 117.8 0.1545 98359.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 849 2 222772.23105889576 1.2642\n",
      "8 yoyos for tract,wedge,wedgePop,r= 849 2 166532.84020848759 0.6321\n",
      "9 yoyos for tract,wedge,wedgePop,r= 849 2 209378.2766215848 1.17\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 852 4.0 3 90.0 0.3844 101153.0\n",
      "I am working on tract number 860 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 862 4.0 0 102.4 0.2085 156497.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 862 5.0 0 102.4 0.2424 156497.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 865 4.0 0 113.2 0.1694 124648.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 866 6.0 0 122.6 0.219 91365.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 869 2.0 1 90.0 0.1473 46356.0\n",
      "I am working on tract number 880 of 5160 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 883\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 885 10.0 3 51.1 1.1346 133285.7\n",
      "I am working on tract number 900 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 909 5.0 2 90.0 1.2714 133618.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 912 7.0 0 132.9 0.229 61181.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 916 7.0 3 90.0 1.729 170316.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 916 8.0 3 90.0 1.8914 170316.7\n",
      "I am working on tract number 920 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 925 4.0 0 115.5 0.2889 138951.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 933 7.0 0 83.3 0.5873 283647.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 936 8.0 0 83.5 0.5204 245791.5\n",
      "I am working on tract number 940 of 5160 tracts\n",
      "I am working on tract number 960 of 5160 tracts\n",
      "I am working on tract number 980 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 991 4.0 0 128.2 0.2528 90264.7\n",
      "I am working on tract number 1000 of 5160 tracts\n",
      "I am working on tract number 1020 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1039 9.0 3 43.7 2.1396 240234.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1039 10.0 3 43.7 2.1984 240234.4\n",
      "I am working on tract number 1040 of 5160 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1043 0 347747.22799224313 1.5396\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1043 0 2898.9764846062753 0.7698\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1043 0 347384.0902294617 1.4468\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1043 0 13407.49923787202 1.1083\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1043 0 345119.03649371857 1.2775\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1043 0 181135.90685918625 1.1929\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1043 0 293435.8471816104 1.2352\n",
      "14 yoyos for tract,wedge,wedgePop,r= 1043 0 241512.08837704884 1.2141\n",
      "15 yoyos for tract,wedge,wedgePop,r= 1043 0 271117.6803811805 1.2246\n",
      "15 yoyos for tract,wedge,wedgePop,r= 1043 0 256447.46176384768 1.2194\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1045 4.0 3 46.4 0.7382 53749.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1046 6.0 0 135.4 1.819 55620.4\n",
      "I am working on tract number 1060 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1061 11.0 0 92.3 0.3651 186292.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1061 12.0 0 92.3 0.3739 186292.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1061 13.0 0 92.3 0.3829 186292.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1061 14.0 0 92.3 0.3921 186292.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1061 15.0 0 92.3 0.4015 186292.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1061 16.0 0 92.3 0.4111 186292.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1061 17.0 0 92.3 0.421 186292.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1061 18.0 0 92.3 0.4311 186292.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1061 19.0 0 92.3 0.4414 186292.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1061 20.0 0 92.3 0.452 186292.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1061 21.0 0 92.3 0.4629 186292.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1061 22.0 0 92.3 0.474 186292.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1061 23.0 0 92.3 0.4854 186292.6\n",
      "we have 2 non-opposing shorted wedges for tract no 1077\n",
      "I am working on tract number 1080 of 5160 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1084\n",
      "we have 2 non-opposing shorted wedges for tract no 1085\n",
      "we have 2 non-opposing shorted wedges for tract no 1089\n",
      "we have 2 non-opposing shorted wedges for tract no 1090\n",
      "we have 2 non-opposing shorted wedges for tract no 1091\n",
      "I am working on tract number 1100 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1100 7.0 0 118.6 0.538 111949.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1100 8.0 0 118.6 0.7887 111949.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1107 8.0 1 90.0 0.6623 187472.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1107 9.0 1 90.0 0.675 187472.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1107 10.0 1 90.0 0.688 187472.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1107 11.0 1 90.0 0.7012 187472.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1107 12.0 1 90.0 0.7147 187472.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1107 13.0 1 90.0 0.7284 187472.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1107 14.0 1 90.0 0.7424 187472.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1107 15.0 1 90.0 0.7567 187472.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1107 16.0 1 90.0 0.7712 187472.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1107 17.0 1 90.0 0.786 187472.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1107 18.0 1 90.0 0.8011 187472.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1107 19.0 1 90.0 0.8165 187472.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1107 20.0 1 90.0 0.8322 187472.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1107 21.0 1 90.0 0.8482 187472.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1107 22.0 1 90.0 0.8645 187472.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1107 23.0 1 90.0 0.8811 187472.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1107 24.0 1 90.0 0.898 187472.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1107 25.0 1 90.0 0.9152 187472.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1107 26.0 1 90.0 0.9328 187472.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1107 27.0 1 90.0 0.9507 187472.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1107 28.0 1 90.0 0.969 187472.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1107 29.0 1 90.0 0.9876 187472.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1107 30.0 1 90.0 1.0066 187472.3\n",
      "loop31.0, tr1107,wedgePops764079.38, 192180.4, 187472.3, 192524.4, 191902.3, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?0010 \n",
      "   targetWP, latest drx4 are tWP,dr, 192304.9,0.0005, 192304.9,0.0193, 192304.9,-0.0009, 192304.9,0.0038\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1107 31.0 1 90.0 1.0259 187472.3\n",
      "loop32.0, tr1107,wedgePops764079.38, 192180.4, 187472.3, 192524.4, 191902.3, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?0010 \n",
      "   targetWP, latest drx4 are tWP,dr, 192304.9,0.0005, 192304.9,0.0197, 192304.9,-0.0009, 192304.9,0.0038\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1107 32.0 1 90.0 1.0456 187472.3\n",
      "loop33.0, tr1107,wedgePops764079.38, 192180.4, 187472.3, 192524.4, 191902.3, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?0010 \n",
      "   targetWP, latest drx4 are tWP,dr, 192304.9,0.0005, 192304.9,0.0201, 192304.9,-0.0009, 192304.9,0.0038\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1107 33.0 1 90.0 1.0657 187472.3\n",
      "loop34.0, tr1107,wedgePops764079.38, 192180.4, 187472.3, 192524.4, 191902.3, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?0010 \n",
      "   targetWP, latest drx4 are tWP,dr, 192304.9,0.0005, 192304.9,0.0205, 192304.9,-0.0009, 192304.9,0.0038\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1107 34.0 1 90.0 1.0862 187472.3\n",
      "loop35.0, tr1107,wedgePops764079.38, 192180.4, 187472.3, 192524.4, 191902.3, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?0010 \n",
      "   targetWP, latest drx4 are tWP,dr, 192304.9,0.0005, 192304.9,0.0209, 192304.9,-0.0009, 192304.9,0.0038\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1107 35.0 1 90.0 1.1071 187472.3\n",
      "loop36.0, tr1107,wedgePops44.1, 11.0, 11.0, 11.0, 11.0, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 192304.9,0.0014, 192304.9,0.0014, 192304.9,0.0014, 192304.9,0.0014\n",
      "loop37.0, tr1107,wedgePops316412.43, 94910.2, 81847.1, 47099.4, 92555.7, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 192304.9,0.1422, 192304.9,0.1422, 192304.9,0.1422, 192304.9,0.1422\n",
      "loop38.0, tr1107,wedgePops506437.74, 110472.1, 100502.5, 108385.8, 187077.3, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 192304.9,0.0486, 192304.9,0.0612, 192304.9,0.1173, 192304.9,0.0507\n",
      "loop39.0, tr1107,wedgePops599717.22, 121868.3, 156481.6, 131165.6, 190201.7, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 192304.9,0.1266, 192304.9,0.1428, 192304.9,0.083, 192304.9,0.0019\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 0.31884 110472.1181 121868.3187 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 0.34755 100502.5116 156481.6137 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.34389 108385.7608 131165.5558 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 0.19623 187077.3499 190201.7315 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loop40.0, tr1107,wedgePops686372.83, 121868.3, 193778.6, 178888.2, 191837.7, Overedge?1, 0, 0, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 215783.7,0.3115, 215783.7,0.053, 215783.7,0.1273, 215783.7,0.001\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 0.63036 121868.3187 121868.3187 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 0.40057 156481.6137 193778.5986 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.47121 131165.5558 178888.2233 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 0.19727 190201.7315 191837.688 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 1107, giving up w pop 686372.8286158283\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1116 1 309379.5981643704 0.7537\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1116 1 170819.6970819587 0.3768\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1116 1 309371.816451019 0.7532\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1116 1 279970.14902269363 0.565\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1116 1 251656.29377136898 0.4709\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1116 1 214371.6576997909 0.4239\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1116 1 187413.3158534328 0.4004\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1116 1 199583.9174084302 0.4121\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1118 5.0 1 26.5 0.3033 40866.9\n",
      "I am working on tract number 1120 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1120 3.0 1 90.0 0.3599 78886.8\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1122 1 356037.45346941566 0.9703\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1122 1 65711.22990856558 0.4852\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1122 1 356268.42873728345 0.9712\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1122 1 224054.72053620336 0.7282\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1122 1 87608.9946415335 0.6067\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1122 1 142775.69802508177 0.6674\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1122 1 186395.05048119556 0.6978\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1122 1 205364.88818747882 0.713\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1123 7.0 0 121.7 0.7283 100303.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1127 8.0 0 104.3 0.6092 150644.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1127 9.0 0 104.3 0.7278 150644.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1131 5.0 0 87.0 0.2215 62536.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1132 8.0 0 115.2 0.3586 178637.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1132 9.0 0 115.2 0.3788 178637.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1132 10.0 0 115.2 0.4001 178637.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1132 11.0 0 115.2 0.4227 178637.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1132 12.0 0 115.2 0.4465 178637.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1132 13.0 0 115.2 0.4716 178637.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1132 14.0 0 115.2 0.4982 178637.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1132 15.0 0 115.2 0.5262 178637.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1132 16.0 0 115.2 0.5559 178637.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1132 17.0 0 115.2 0.5872 178637.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1133 5.0 2 90.0 0.3883 159949.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1133 6.0 2 90.0 0.4445 159949.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1133 7.0 2 90.0 0.5088 159949.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1135 7.0 0 90.0 0.6175 179229.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1135 8.0 0 90.0 0.6507 179229.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1135 9.0 0 90.0 0.6857 179229.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1135 10.0 0 90.0 0.7225 179229.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1135 11.0 0 90.0 0.7613 179229.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1135 12.0 0 90.0 0.8023 179229.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1135 13.0 0 90.0 0.8454 179229.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1135 14.0 0 90.0 0.8908 179229.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1135 15.0 0 90.0 0.9387 179229.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1135 16.0 0 90.0 0.9892 179229.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1135 17.0 0 90.0 1.0424 179229.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1135 18.0 0 90.0 1.0984 179229.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1136 5.0 0 139.4 0.3745 32598.4\n",
      "I am working on tract number 1140 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1140 6.0 0 132.5 0.3007 59309.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1141 5.0 0 138.6 0.2647 33710.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1143 3.0 3 90.0 1.0572 43139.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1146 4.0 0 139.1 0.4611 31902.7\n",
      "I am working on tract number 1160 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1160 7.0 0 97.7 0.6202 196854.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1164 5.0 3 90.0 2.002 88533.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1172 2.0 2 90.0 0.7546 164697.1\n",
      "I am working on tract number 1180 of 5160 tracts\n",
      "I am working on tract number 1200 of 5160 tracts\n",
      "I am working on tract number 1220 of 5160 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1223\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1225 8.0 0 141.1 0.301 28703.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1227 5.0 0 137.5 0.1962 41910.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1239 5.0 0 132.6 0.2191 65229.0\n",
      "I am working on tract number 1240 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1254 4.0 0 135.2 0.2249 54785.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1257 4.0 2 90.0 0.7505 353888.7\n",
      "I am working on tract number 1260 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1267 10.0 0 98.0 0.2244 168206.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1267 11.0 0 98.0 0.2477 168206.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1270 8.0 0 110.8 0.2017 137414.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1271 8.0 0 106.7 0.1651 151063.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1271 9.0 0 106.7 0.1969 151063.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1271 10.0 0 106.7 0.2348 151063.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1272 4.0 0 136.3 0.2359 46520.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1277 13.0 0 90.8 0.1596 189001.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1277 14.0 0 90.8 0.1617 189001.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1277 15.0 0 90.8 0.1638 189001.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1277 16.0 0 90.8 0.166 189001.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1278 8.0 0 100.5 0.2075 162446.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1278 9.0 0 100.5 0.2349 162446.2\n",
      "I am working on tract number 1280 of 5160 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1290 2 239959.0119755299 1.1198\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1290 2 92938.39736183925 0.5599\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1290 2 239953.29694445714 1.1197\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1290 2 126905.68347025402 0.8398\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1290 2 213700.5753388541 0.9797\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1290 2 163681.03623071202 0.9098\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1291 3.0 0 120.7 0.6181 154664.0\n",
      "I am working on tract number 1300 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1304 4.0 0 116.8 0.1838 122277.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1306 5.0 0 128.5 0.2292 73507.2\n",
      "we have 2 non-opposing shorted wedges for tract no 1314\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1315 2.0 0 90.0 0.1184 26564.0\n",
      "I am working on tract number 1320 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1332 4.0 0 136.8 0.2056 41440.3\n",
      "I am working on tract number 1340 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1347 6.0 3 90.0 0.2722 183072.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1347 7.0 3 90.0 0.2824 183072.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1351 4.0 0 143.9 0.9732 1501.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1358 4.0 0 143.3 1.0209 4538.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1358 5.0 0 143.3 1.7581 4538.6\n",
      "I am working on tract number 1360 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1364 2.0 3 90.0 0.2534 46763.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 4.0 0 142.3 0.1684 15833.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 7.0 3 37.7 0.2622 240434.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 8.0 3 37.7 0.2708 240434.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 11.0 3 37.7 0.904 255279.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 12.0 3 37.7 0.8929 255279.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 13.0 3 37.7 0.882 255279.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 14.0 3 37.7 0.8712 255279.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 15.0 3 37.7 0.8605 255279.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 16.0 3 37.7 0.85 255279.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 17.0 3 37.7 0.8396 255279.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 18.0 3 37.7 0.8293 255279.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 19.0 3 37.7 0.8191 255279.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 20.0 3 37.7 0.8091 255279.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 21.0 3 37.7 0.7992 255279.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 22.0 3 37.7 0.7894 255279.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 23.0 3 37.7 0.7797 255279.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 24.0 3 37.7 0.7702 255279.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 25.0 3 37.7 0.7608 255279.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 26.0 3 37.7 0.7514 255279.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 27.0 3 37.7 0.7422 255279.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 28.0 3 37.7 0.7332 255279.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 29.0 3 37.7 0.7242 255279.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 30.0 3 37.7 0.7153 255279.3\n",
      "loop31.0, tr1370,wedgePops773435.93, 15833.7, 251315.0, 251007.9, 255279.3, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0101 \n",
      "   targetWP, latest drx4 are tWP,dr, 251128.6,0.6276, 251128.6,-0.0005, 251128.6,0.0002, 251128.6,-0.0088\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 31.0 3 37.7 0.7066 255279.3\n",
      "loop32.0, tr1370,wedgePops773435.93, 15833.7, 251315.0, 251007.9, 255279.3, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0101 \n",
      "   targetWP, latest drx4 are tWP,dr, 251128.6,0.6276, 251128.6,-0.0005, 251128.6,0.0002, 251128.6,-0.0087\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 32.0 3 37.7 0.6979 255279.3\n",
      "loop33.0, tr1370,wedgePops773435.93, 15833.7, 251315.0, 251007.9, 255279.3, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0101 \n",
      "   targetWP, latest drx4 are tWP,dr, 251128.6,0.6276, 251128.6,-0.0005, 251128.6,0.0002, 251128.6,-0.0085\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 33.0 3 37.7 0.6894 255279.3\n",
      "loop34.0, tr1370,wedgePops773435.93, 15833.7, 251315.0, 251007.9, 255279.3, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0101 \n",
      "   targetWP, latest drx4 are tWP,dr, 251128.6,0.6276, 251128.6,-0.0005, 251128.6,0.0002, 251128.6,-0.0084\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 34.0 3 37.7 0.6809 255279.3\n",
      "loop35.0, tr1370,wedgePops773435.93, 15833.7, 251315.0, 251007.9, 255279.3, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0101 \n",
      "   targetWP, latest drx4 are tWP,dr, 251128.6,0.6276, 251128.6,-0.0005, 251128.6,0.0002, 251128.6,-0.0083\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 35.0 3 37.7 0.6726 255279.3\n",
      "loop36.0, tr1370,wedgePops773435.93, 15833.7, 251315.0, 251007.9, 255279.3, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0101 \n",
      "   targetWP, latest drx4 are tWP,dr, 251128.6,0.6276, 251128.6,-0.0005, 251128.6,0.0002, 251128.6,-0.0082\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 36.0 3 37.7 0.6643 255279.3\n",
      "loop37.0, tr1370,wedgePops773435.93, 15833.7, 251315.0, 251007.9, 255279.3, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0101 \n",
      "   targetWP, latest drx4 are tWP,dr, 251128.6,0.6276, 251128.6,-0.0005, 251128.6,0.0002, 251128.6,-0.0081\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 37.0 3 37.7 0.6562 255279.3\n",
      "loop38.0, tr1370,wedgePops773435.93, 15833.7, 251315.0, 251007.9, 255279.3, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0101 \n",
      "   targetWP, latest drx4 are tWP,dr, 251128.6,0.6276, 251128.6,-0.0005, 251128.6,0.0002, 251128.6,-0.008\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 38.0 3 37.7 0.6482 255279.3\n",
      "loop39.0, tr1370,wedgePops773435.93, 15833.7, 251315.0, 251007.9, 255279.3, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0101 \n",
      "   targetWP, latest drx4 are tWP,dr, 251128.6,0.6276, 251128.6,-0.0005, 251128.6,0.0002, 251128.6,-0.0079\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 0.79599 15833.7291 15833.7291 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 0.24633 252060.4856 251314.9761 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.17469 250467.0417 251007.8998 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 0.64024 255279.3249 255279.3249 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1370 39.0 3 37.7 0.6402 255279.3\n",
      "loop40.0, tr1370,wedgePops773435.93, 15833.7, 251315.0, 251007.9, 255279.3, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0101 \n",
      "   targetWP, latest drx4 are tWP,dr, 251128.6,0.6276, 251128.6,-0.0005, 251128.6,0.0002, 251128.6,-0.0078\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 0.79599 15833.7291 15833.7291 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 0.24633 252060.4856 251314.9761 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.17469 250467.0417 251007.8998 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 0.6324 255279.3249 255279.3249 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 1370, giving up w pop 773435.9297963935\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1378 4.0 0 137.5 0.4579 32020.1\n",
      "I am working on tract number 1380 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1384 5.0 0 90.0 1.2148 154874.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1385 5.0 3 90.0 0.2719 168427.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1385 6.0 3 90.0 0.2998 168427.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1385 7.0 3 90.0 0.3306 168427.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1385 8.0 3 90.0 0.3646 168427.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1385 9.0 3 90.0 0.4021 168427.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1385 10.0 3 90.0 0.4434 168427.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1385 11.0 3 90.0 0.489 168427.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1385 12.0 3 90.0 0.5393 168427.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1386 4.0 2 90.0 0.3632 129009.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1386 5.0 2 90.0 0.4835 129009.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1387 5.0 0 90.0 0.332 158815.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1387 6.0 0 90.0 0.382 158815.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1387 7.0 0 90.0 0.4396 158815.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1388 6.0 1 90.0 1.1911 150672.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1388 7.0 1 90.0 1.423 150672.9\n",
      "I am working on tract number 1400 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1402 2.0 2 90.0 0.4448 20037.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1402 5.0 0 136.0 0.4005 37501.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1403 6.0 0 132.8 0.3233 52583.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1409 6.0 0 117.1 0.6428 178798.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1409 7.0 0 117.1 0.6786 178798.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1409 8.0 0 117.1 0.7163 178798.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1409 9.0 0 117.1 0.7562 178798.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1409 10.0 0 117.1 0.7982 178798.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1409 11.0 0 117.1 0.8426 178798.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1409 12.0 0 117.1 0.8895 178798.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1409 13.0 0 117.1 0.939 178798.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1411 3.0 3 90.0 0.5147 136384.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1417 2.0 0 90.0 0.3451 173092.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1417 3.0 0 90.0 0.373 173092.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1417 4.0 0 90.0 0.4033 173092.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1417 5.0 0 90.0 0.4359 173092.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1417 6.0 0 90.0 0.4713 173092.2\n",
      "I am working on tract number 1420 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1430 6.0 2 90.0 1.3744 22754.1\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1436 0 507437.75190306455 4.6638\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1436 0 47734.37712743151 2.3319\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1436 0 500430.07150251715 4.5927\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1436 0 371349.6272908938 3.4623\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1436 0 65452.138618967205 2.8971\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1436 0 96130.95309225886 3.1797\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1437 4.0 0 90.0 0.5236 135086.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1437 5.0 0 90.0 0.6753 135086.1\n",
      "I am working on tract number 1440 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1456 7.0 3 79.4 1.6324 98937.8\n",
      "I am working on tract number 1460 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1477 2.0 2 90.0 0.3428 15226.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1478 2.0 0 90.0 0.4057 17620.6\n",
      "I am working on tract number 1480 of 5160 tracts\n",
      "we have 2 OPPOSING shorted wedges for tract no 1490\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1492 1 222444.9530799454 3.3797\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1492 1 37253.48348209486 1.6898\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1492 1 422913.1510991836 3.6757\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1492 1 71837.48736268212 2.6827\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1492 1 100308.41689903525 3.1792\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1492 1 291608.84306454536 3.4274\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1492 1 142120.39731755984 3.3033\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1492 1 202632.85680634415 3.3654\n",
      "14 yoyos for tract,wedge,wedgePop,r= 1492 1 164146.67955281946 3.3343\n",
      "14 yoyos for tract,wedge,wedgePop,r= 1492 1 182492.82121737863 3.3499\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1498 2.0 3 90.0 0.3392 16249.3\n",
      "I am working on tract number 1500 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1506 2.0 3 90.0 0.5996 4054.7\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1509 0 412876.8077544967 1.8521\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1509 0 75158.97400836973 0.9261\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1509 0 430092.03562952345 1.9207\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1509 0 210737.5887936457 1.4234\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1509 0 90320.15505044191 1.1747\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1509 0 99355.38486943289 1.299\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1509 0 114813.1602769729 1.3612\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1509 0 147557.93763010297 1.3923\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1509 0 179858.20763751838 1.4078\n",
      "I am working on tract number 1520 of 5160 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1523 2 662550.4914906658 1.0519\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1538 10.0 0 109.8 1.6272 191845.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1538 11.0 0 109.8 1.7476 191845.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1539 3.0 2 90.0 0.304 81106.9\n",
      "I am working on tract number 1540 of 5160 tracts\n",
      "I am working on tract number 1560 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1562 2.0 2 90.0 0.3034 24281.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1562 5.0 1 30.8 0.3366 40247.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1578 2.0 0 90.0 0.3393 17768.0\n",
      "we have 2 non-opposing shorted wedges for tract no 1578\n",
      "I am working on tract number 1580 of 5160 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1586 2 374270.44479594775 3.0908\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1586 2 30012.854218024295 1.5454\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1586 2 415053.2237358263 3.5463\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1586 2 327237.50376521714 2.5459\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1586 2 43995.7510454086 2.0456\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1586 2 88343.9734478027 2.2958\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1586 2 277857.61972476245 2.4208\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1586 2 168701.587662598 2.3583\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1586 2 223011.1755016126 2.3895\n",
      "14 yoyos for tract,wedge,wedgePop,r= 1586 2 195103.754482839 2.3739\n",
      "we have 2 non-opposing shorted wedges for tract no 1588\n",
      "we have 2 non-opposing shorted wedges for tract no 1590\n",
      "we have 2 non-opposing shorted wedges for tract no 1592\n",
      "I am working on tract number 1600 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1608 2.0 1 90.0 0.33 24216.1\n",
      "we have 2 non-opposing shorted wedges for tract no 1608\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1610 2.0 1 90.0 0.2687 32844.0\n",
      "I am working on tract number 1620 of 5160 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1623\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1633 2.0 1 90.0 0.7352 196099.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1633 3.0 1 90.0 0.7244 196099.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1633 4.0 1 90.0 0.7139 196099.5\n",
      "I am working on tract number 1640 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1645 2.0 0 90.0 0.385 72000.4\n",
      "I am working on tract number 1660 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1667 3.0 1 90.0 0.9808 39373.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1669 2.0 2 90.0 0.3535 18835.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1670 5.0 2 284.0 0.4851 27289.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1672 0 336419.6704323605 3.0966\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1672 0 11920.416104171134 1.5483\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1672 0 500356.0454834882 4.6038\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1672 0 331193.8423296859 3.0761\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1672 0 17098.409028739552 2.3122\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1672 0 31840.43249926908 2.6941\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1672 0 112938.04645898237 2.8851\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1672 0 250879.34262714398 2.9806\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1672 0 173635.79934504107 2.9328\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1672 0 209987.5420038749 2.9567\n",
      "I am working on tract number 1680 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1686 3.0 0 141.6 0.8078 40059.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1693 4.0 0 125.5 0.3454 90029.3\n",
      "I am working on tract number 1700 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1713 4.0 0 133.2 0.314 71431.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1716 4.0 0 129.6 0.2666 74844.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1718 5.0 0 137.1 1.2649 100819.4\n",
      "I am working on tract number 1720 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1727 2.0 1 90.0 0.6567 102412.8\n",
      "we have 2 non-opposing shorted wedges for tract no 1729\n",
      "we have 2 non-opposing shorted wedges for tract no 1734\n",
      "I am working on tract number 1740 of 5160 tracts\n",
      "I am working on tract number 1760 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1765 4.0 0 133.8 0.3182 56181.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1769 3.0 3 90.0 0.4223 128601.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1769 4.0 3 90.0 0.5635 128601.4\n",
      "I am working on tract number 1780 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1785 2.0 2 90.0 0.9579 725.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1787 5.0 0 118.3 0.2783 110363.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1790 3.0 0 90.0 0.4246 64964.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1792 5.0 0 130.9 0.199 77431.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1797 4.0 0 97.3 0.2231 167447.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1798 4.0 0 137.2 0.249 38643.5\n",
      "I am working on tract number 1800 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1816 2.0 3 90.0 0.4232 129504.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1816 3.0 3 90.0 0.5619 129504.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1816 4.0 3 90.0 0.7461 129504.9\n",
      "I am working on tract number 1820 of 5160 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1827 3 354502.61950694193 0.9918\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1827 3 35582.16935283528 0.4959\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1827 3 348152.32501674036 0.9666\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1827 3 210714.0609817936 0.7313\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1827 3 55816.38065351863 0.6136\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1827 3 102606.10437893716 0.6724\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1827 3 152260.185205677 0.7018\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1827 3 185647.58762321266 0.7165\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1827 3 199563.46561146915 0.7239\n",
      "I am working on tract number 1840 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1851 3.0 0 90.0 0.2693 101890.2\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1856 3 379633.29374020704 1.0912\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1856 3 38432.35129984678 0.5456\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1856 3 379188.7187348532 1.0896\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1856 3 258018.8768890637 0.8176\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1856 3 63597.1951918235 0.6816\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1856 3 147015.32684360145 0.7496\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1856 3 214645.1120146524 0.7836\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1856 3 183444.45368455048 0.7666\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1856 3 199472.93042858937 0.7751\n",
      "I am working on tract number 1860 of 5160 tracts\n",
      "I am working on tract number 1880 of 5160 tracts\n",
      "we have 2 OPPOSING shorted wedges for tract no 1889\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1898 4.0 0 90.0 1.3198 164219.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1898 5.0 0 90.0 1.4824 164219.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1898 6.0 0 90.0 1.665 164219.5\n",
      "I am working on tract number 1900 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1910 3.0 0 143.5 1.4864 4027.9\n",
      "I am working on tract number 1920 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1924 3.0 3 90.0 1.2075 21812.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1924 7.0 0 140.5 1.1914 28294.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1925 7.0 0 133.9 1.1216 76861.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1931 10.0 0 136.3 1.2543 175022.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1931 11.0 0 136.3 1.345 175022.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1931 12.0 0 136.3 1.4423 175022.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1931 13.0 0 136.3 1.5466 175022.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1931 14.0 0 136.3 1.6584 175022.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1931 15.0 0 136.3 1.7784 175022.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1932 12.0 0 125.7 1.2941 149582.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1938 12.0 0 137.5 0.9588 187865.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1938 13.0 0 137.5 0.9757 187865.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1938 14.0 0 137.5 0.9929 187865.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1938 15.0 0 137.5 1.0104 187865.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1938 16.0 0 137.5 1.0282 187865.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1938 17.0 0 137.5 1.0463 187865.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1938 18.0 0 137.5 1.0647 187865.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1938 19.0 0 137.5 1.0835 187865.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1938 20.0 0 137.5 1.1026 187865.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1938 21.0 0 137.5 1.122 187865.2\n",
      "I am working on tract number 1940 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1944 2.0 3 90.0 0.522 15249.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1945 10.0 0 128.4 1.5601 163569.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1947 7.0 2 90.0 0.753 182869.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1947 8.0 2 90.0 0.7818 182869.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1952 7.0 3 50.7 1.4052 105867.8\n",
      "I am working on tract number 1960 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1961 8.0 0 117.3 0.438 138680.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1961 9.0 0 117.3 0.5547 138680.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1975 2.0 1 90.0 0.4061 52166.1\n",
      "I am working on tract number 1980 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1992 4.0 0 90.0 0.449 145683.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1992 11.0 0 98.0 0.471 183228.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1992 12.0 0 98.0 0.4883 183228.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1992 13.0 0 98.0 0.5062 183228.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1992 14.0 0 98.0 0.5248 183228.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1992 15.0 0 98.0 0.544 183228.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1993 6.0 0 138.7 1.1471 37497.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1995 6.0 0 138.6 0.8174 41799.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1999 3.0 0 143.9 1.2599 428.0\n",
      "I am working on tract number 2000 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2000 6.0 0 137.6 0.6408 49081.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2004 8.0 0 132.1 0.5779 93304.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2004 9.0 0 132.1 0.9555 93304.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2007 7.0 2 90.0 0.6597 177159.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2007 8.0 2 90.0 0.7012 177159.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2007 9.0 2 90.0 0.7452 177159.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2007 10.0 2 90.0 0.792 177159.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2009 9.0 3 90.0 0.6158 187618.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2009 10.0 3 90.0 0.6273 187618.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2009 11.0 3 90.0 0.639 187618.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2009 12.0 3 90.0 0.6509 187618.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2009 13.0 3 90.0 0.663 187618.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2009 14.0 3 90.0 0.6753 187618.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2009 15.0 3 90.0 0.6879 187618.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2009 16.0 3 90.0 0.7007 187618.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2009 17.0 3 90.0 0.7138 187618.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2009 18.0 3 90.0 0.727 187618.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2009 19.0 3 90.0 0.7406 187618.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2009 20.0 3 90.0 0.7544 187618.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2009 21.0 3 90.0 0.7684 187618.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2009 22.0 3 90.0 0.7827 187618.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2009 23.0 3 90.0 0.7973 187618.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2009 24.0 3 90.0 0.8121 187618.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2009 25.0 3 90.0 0.8272 187618.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2009 26.0 3 90.0 0.8426 187618.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2009 27.0 3 90.0 0.8583 187618.9\n",
      "loop31.0, tr2009,wedgePops598199.88, 168365.9, 151618.5, 115222.5, 162993.0, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 192304.9,0.0269, 192304.9,0.063, 192304.9,0.086, 192304.9,0.0207\n",
      "loop32.0, tr2009,wedgePops666957.0, 178976.8, 172515.1, 140173.6, 175291.5, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 192304.9,0.0156, 192304.9,0.0408, 192304.9,0.1549, 192304.9,0.033\n",
      "loop33.0, tr2009,wedgePops738469.52, 188040.6, 182664.3, 187722.5, 180042.1, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 192304.9,0.0146, 192304.9,0.0276, 192304.9,0.1721, 192304.9,0.0316\n",
      "loop34.0, tr2009,wedgePops755821.08, 190571.0, 188304.1, 188379.0, 188567.0, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 192304.9,0.0052, 192304.9,0.0195, 192304.9,0.0113, 192304.9,0.0549\n",
      "loop35.0, tr2009,wedgePops765232.83, 191809.7, 190642.1, 191468.9, 191312.2, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 192304.9,0.0028, 192304.9,0.0106, 192304.9,0.0509, 192304.9,0.0171\n",
      "loop36.0, tr2009,wedgePops768297.61, 192197.6, 191586.3, 192170.7, 192343.1, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 192304.9,0.0009, 192304.9,0.0059, 192304.9,0.0105, 192304.9,0.0048\n",
      "I am working on tract number 2020 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2031 4.0 2 90.0 1.2494 108773.7\n",
      "I am working on tract number 2040 of 5160 tracts\n",
      "I am working on tract number 2060 of 5160 tracts\n",
      "I am working on tract number 2080 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2093 10.0 0 104.8 0.3261 178783.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2093 11.0 0 104.8 0.3443 178783.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2093 12.0 0 104.8 0.3635 178783.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2093 13.0 0 104.8 0.3837 178783.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2093 14.0 0 104.8 0.4051 178783.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2093 15.0 0 104.8 0.4276 178783.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2093 16.0 0 104.8 0.4514 178783.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2093 17.0 0 104.8 0.4766 178783.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2093 18.0 0 104.8 0.5031 178783.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2093 19.0 0 104.8 0.5311 178783.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2096 10.0 0 99.9 0.2816 179440.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2096 11.0 0 99.9 0.2965 179440.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2096 12.0 0 99.9 0.3122 179440.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2096 13.0 0 99.9 0.3287 179440.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2096 14.0 0 99.9 0.3461 179440.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2096 15.0 0 99.9 0.3643 179440.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2096 16.0 0 99.9 0.3836 179440.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2096 17.0 0 99.9 0.4039 179440.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2096 18.0 0 99.9 0.4252 179440.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2096 19.0 0 99.9 0.4477 179440.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2096 20.0 0 99.9 0.4713 179440.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2096 21.0 0 99.9 0.4962 179440.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2097 3.0 0 90.0 0.7066 51840.1\n",
      "I am working on tract number 2100 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2105 5.0 2 285.8 0.5801 6366.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2110 2.0 1 90.0 0.4604 45823.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2112 7.0 0 135.8 0.5981 54887.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2114 2.0 3 90.0 0.3646 17428.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2114 7.0 0 135.4 1.2775 83450.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2115 2.0 2 90.0 0.3706 58839.5\n",
      "I am working on tract number 2120 of 5160 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2126 0 403676.72115074884 1.5996\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2126 0 97451.98974707176 0.7998\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2126 0 403699.397980402 1.5997\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2126 0 182443.80891067613 1.1998\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2126 0 356804.75192711665 1.3997\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2126 0 312961.1970703884 1.2998\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2126 0 261621.52722484656 1.2498\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2126 0 228081.36099943117 1.2248\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2126 0 207086.13007260693 1.2123\n",
      "we have 2 non-opposing shorted wedges for tract no 2132\n",
      "we have 2 non-opposing shorted wedges for tract no 2134\n",
      "we have 2 non-opposing shorted wedges for tract no 2135\n",
      "we have 2 non-opposing shorted wedges for tract no 2136\n",
      "we have 2 non-opposing shorted wedges for tract no 2138\n",
      "we have 2 non-opposing shorted wedges for tract no 2139\n",
      "I am working on tract number 2140 of 5160 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2145 2 509619.95019079535 4.1584\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2145 2 93338.04521475383 2.0792\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2145 2 528444.3157154864 4.3764\n",
      "we have 2 non-opposing shorted wedges for tract no 2147\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2150 7.0 0 112.3 0.5334 128549.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2152 6.0 0 110.5 0.2175 128953.0\n",
      "I am working on tract number 2160 of 5160 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2177 3 269991.9937907354 1.7913\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2177 3 17272.565811283537 0.8956\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2177 3 301179.21049787774 2.0466\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2177 3 46607.674649122026 1.4711\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2177 3 262982.06351152214 1.7589\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2178 3 382480.442399058 2.1741\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2178 3 31189.707429659727 1.087\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2178 3 386126.48288410273 2.3291\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2178 3 342357.7591679273 1.7081\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2178 3 53660.641012017964 1.3976\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2178 3 145247.05649088795 1.5528\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2178 3 292597.7190043718 1.6304\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2178 3 224907.77710650448 1.5916\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2178 3 176172.10314622315 1.5722\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2178 3 197889.67018612966 1.5819\n",
      "14 yoyos for tract,wedge,wedgePop,r= 2178 3 186660.0887296934 1.5771\n",
      "I am working on tract number 2180 of 5160 tracts\n",
      "we have 2 OPPOSING shorted wedges for tract no 2192\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2194 4.0 0 108.5 0.2358 145108.6\n",
      "I am working on tract number 2200 of 5160 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2210\n",
      "we have 2 non-opposing shorted wedges for tract no 2213\n",
      "I am working on tract number 2220 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2220 2.0 2 90.0 0.2756 48601.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2221 2.0 0 90.0 0.4346 57768.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2221 5.0 0 122.2 0.386 92064.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2222 2.0 3 90.0 0.394 17409.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2223 18.0 1 75.9 2.1057 1233929.4\n",
      "we have 2 non-opposing shorted wedges for tract no 2226\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2226 2 289896.80936740345 1.6334\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2226 2 80114.42657595023 0.8167\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2226 2 413140.50944952626 1.6515\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2226 2 105695.6647639951 1.2341\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2226 2 127490.37421237622 1.4428\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2226 2 158441.15665771515 1.5472\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2226 2 203005.79813060994 1.5993\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2226 2 251072.79144172044 1.6254\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2226 2 319498.50541060534 1.6385\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2233 5.0 0 89.6 0.2743 191444.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2233 6.0 0 89.6 0.2752 191444.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2233 7.0 0 89.6 0.2762 191444.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2233 8.0 0 89.6 0.3243 191444.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2233 9.0 0 89.6 0.3809 191444.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2233 10.0 0 89.6 0.4473 191444.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2234 9.0 3 61.8 0.3493 166423.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2234 10.0 3 61.8 0.3885 166423.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2234 11.0 3 61.8 0.4322 166423.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2237 6.0 0 116.8 0.3253 101175.9\n",
      "I am working on tract number 2240 of 5160 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2245\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2246 8.0 3 52.6 0.3545 194607.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2246 9.0 3 52.6 0.4028 194607.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2246 10.0 3 52.6 0.4576 194607.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2253 2.0 1 90.0 0.314 34343.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2256 2.0 1 90.0 0.3072 151025.3\n",
      "we have 2 non-opposing shorted wedges for tract no 2259\n",
      "I am working on tract number 2260 of 5160 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2263 2 227618.1548112322 1.7662\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2263 2 89749.33220956285 0.8831\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2263 2 483808.73220993136 1.8298\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2263 2 120342.87204529723 1.3565\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2263 2 148039.8146892977 1.5931\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2263 2 180904.858783125 1.7115\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2263 2 233550.91687232835 1.7706\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2263 2 200674.1185948475 1.7411\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2263 2 214730.84323522978 1.7558\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2266 4.0 0 133.1 0.2167 57720.8\n",
      "I am working on tract number 2280 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2298 2.0 0 90.0 0.1357 140941.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2298 3.0 0 90.0 0.1699 140941.2\n",
      "I am working on tract number 2300 of 5160 tracts\n",
      "I am working on tract number 2320 of 5160 tracts\n",
      "I am working on tract number 2340 of 5160 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2346\n",
      "I am working on tract number 2360 of 5160 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2361\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2364 2.0 2 90.0 0.4231 21789.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2373 2.0 3 90.0 0.3423 66062.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2378 4.0 0 136.7 0.2284 44755.5\n",
      "I am working on tract number 2380 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2385 2.0 3 90.0 0.3667 11999.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2386 2.0 3 90.0 0.1307 46655.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2396 2.0 2 90.0 0.1446 94023.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2396 6.0 0 109.9 0.1759 138477.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2396 7.0 0 109.9 0.223 138477.3\n",
      "I am working on tract number 2400 of 5160 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2404\n",
      "we have 2 non-opposing shorted wedges for tract no 2406\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2409 2.0 1 90.0 0.7113 212965.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2409 3.0 1 90.0 0.6582 212965.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2409 4.0 1 90.0 0.6091 212965.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2409 5.0 1 90.0 0.5637 212965.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2409 6.0 1 90.0 0.5216 212965.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2409 7.0 1 90.0 0.4827 212965.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2409 8.0 1 90.0 0.4467 212965.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2409 9.0 1 90.0 0.4134 212965.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2409 10.0 1 90.0 0.3825 212965.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2409 11.0 1 90.0 0.354 212965.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2409 12.0 1 90.0 0.3276 212965.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2409 13.0 1 90.0 0.3031 212965.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2409 14.0 1 90.0 0.2805 212965.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2409 15.0 1 90.0 0.2596 212965.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2409 16.0 1 90.0 0.2402 212965.0\n",
      "we have 2 non-opposing shorted wedges for tract no 2412\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2417 6.0 0 114.8 0.2131 113199.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2418 2.0 0 90.0 0.2321 91175.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2418 5.0 0 113.4 0.2186 116319.0\n",
      "I am working on tract number 2420 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2423 6.0 0 96.8 1.2175 18850.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2438 4.0 0 138.9 0.5792 24784.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2438 5.0 0 138.9 1.3164 24784.4\n",
      "I am working on tract number 2440 of 5160 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2444\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2445 4.0 0 143.9 1.0161 585.9\n",
      "I am working on tract number 2460 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2462 3.0 1 -272.4 1.4938 5260.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2463 5.0 0 136.9 0.2593 43562.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2464 4.0 0 136.3 0.2603 43588.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2479 3.0 3 90.0 0.3159 65216.8\n",
      "I am working on tract number 2480 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2483 12.0 0 94.3 0.3693 176473.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2483 13.0 0 94.3 0.3936 176473.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2483 14.0 0 94.3 0.4196 176473.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2483 15.0 0 94.3 0.4472 176473.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2483 16.0 0 94.3 0.4766 176473.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2483 17.0 0 94.3 0.508 176473.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2485 4.0 2 90.0 0.6012 104565.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2485 9.0 0 109.7 0.3831 129824.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2485 10.0 0 109.7 0.5079 129824.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2487 8.0 0 125.5 0.6482 87623.3\n",
      "we have 2 non-opposing shorted wedges for tract no 2490\n",
      "I am working on tract number 2500 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2505 4.0 0 143.0 1.3108 49843.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2514 3.0 3 90.0 0.3222 63461.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2515 0 342649.03997479996 1.1717\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2515 0 121808.51357599485 0.5858\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2515 0 341459.076263902 1.168\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2515 0 287010.6286194921 0.8769\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2515 0 162338.99970963225 0.7314\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2515 0 237472.10913273416 0.8041\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2515 0 199120.62653947354 0.7678\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2515 0 179087.48099495211 0.7496\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2517 2 302281.4025459719 1.0445\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2517 2 137501.37077677716 0.5223\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2517 2 300378.0099730252 1.0352\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2517 2 274390.13491083606 0.7787\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2517 2 180384.44005532877 0.6505\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2517 2 240718.9106962229 0.7146\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2517 2 211306.15234298768 0.6826\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2519 4.0 0 129.8 0.1615 69415.1\n",
      "I am working on tract number 2520 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2522 6.0 0 136.7 0.5609 55904.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2523 5.0 0 140.7 1.2232 25788.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2523 5.0 3 39.3 0.7192 124791.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2525 6.0 3 37.6 1.5337 227706.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2525 7.0 3 37.6 1.6614 227706.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2526 2.0 3 90.0 0.2302 51323.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2526 5.0 0 135.4 0.258 59194.2\n",
      "I am working on tract number 2540 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2548 6.0 0 143.1 1.6989 16034.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2552 4.0 0 143.6 0.9751 3064.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2554 7.0 0 141.8 1.9666 48369.0\n",
      "we have 2 OPPOSING shorted wedges for tract no 2557\n",
      "I am working on tract number 2560 of 5160 tracts\n",
      "I am working on tract number 2580 of 5160 tracts\n",
      "I am working on tract number 2600 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2614 2.0 0 90.0 0.2647 9263.9\n",
      "I am working on tract number 2620 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2620 10.0 3 39.7 1.5604 375926.2\n",
      "we have 2 non-opposing shorted wedges for tract no 2624\n",
      "I am working on tract number 2640 of 5160 tracts\n",
      "I am working on tract number 2660 of 5160 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2667 1 517678.3526004405 2.736\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2667 1 74690.92461982468 1.368\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2667 1 496500.0950234046 2.6106\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2667 1 425509.09144690586 1.9893\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2667 1 248443.68300041385 1.6787\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2667 1 94633.77302988642 1.5233\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2667 1 136719.8837055044 1.601\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2667 1 192133.94912566277 1.6398\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2667 1 220679.1072438379 1.6593\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2667 1 233685.32371978596 1.669\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2668 1 515432.47424708394 2.7304\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2668 1 72575.86053086747 1.3652\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2668 1 493810.8434128157 2.6019\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2668 1 424735.03744852846 1.9835\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2668 1 282810.37332024355 1.6744\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2668 1 98883.65637220026 1.5198\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2668 1 153825.88886206542 1.5971\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2668 1 216255.65019757385 1.6357\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2668 1 243355.84400113946 1.655\n",
      "I am working on tract number 2680 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2685 7.0 0 108.2 0.1601 143295.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2685 8.0 0 108.2 0.1982 143295.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2687 12.0 0 113.8 0.1658 130538.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2687 13.0 0 113.8 0.219 130538.6\n",
      "I am working on tract number 2700 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2700 2.0 2 90.0 0.4581 26039.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2702 5.0 0 119.8 0.5333 130187.3\n",
      "I am working on tract number 2720 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2723 5.0 0 135.4 0.2113 51932.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2727 4.0 0 135.6 0.1872 51156.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2728 4.0 0 139.9 0.2365 32203.3\n",
      "I am working on tract number 2740 of 5160 tracts\n",
      "I am working on tract number 2760 of 5160 tracts\n",
      "I am working on tract number 2780 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2793 5.0 0 124.8 0.3017 86793.7\n",
      "I am working on tract number 2800 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2802 3.0 0 121.6 0.5348 122793.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2804 6.0 0 126.9 0.3457 78777.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2807 7.0 0 115.6 0.2745 111226.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2808 6.0 0 93.4 0.3669 182774.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2808 7.0 0 93.4 0.3811 182774.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2808 8.0 0 93.4 0.3958 182774.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2808 9.0 0 93.4 0.4111 182774.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2808 10.0 0 93.4 0.4269 182774.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2809 6.0 0 113.4 0.2995 110149.2\n",
      "I am working on tract number 2820 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2820 4.0 0 379.9 0.6253 296589.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2820 5.0 0 379.9 0.5573 296589.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2822 5.0 0 374.3 0.6273 294107.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2823 5.0 0 112.6 0.3151 125459.7\n",
      "we have 2 non-opposing shorted wedges for tract no 2835\n",
      "we have 2 non-opposing shorted wedges for tract no 2836\n",
      "I am working on tract number 2840 of 5160 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2858 2 467225.35064468376 3.4157\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2858 2 76962.86568291462 1.7078\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2858 2 444956.74510715844 3.1467\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2858 2 127408.55929399602 2.4273\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2858 2 410764.3373393613 2.787\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2858 2 363895.0996066161 2.6071\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2858 2 212904.53713448023 2.5172\n",
      "loop31.0, tr2858,wedgePops726201.56, 192586.6, 191898.6, 149649.3, 192067.1, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?10110 \n",
      "   targetWP, latest drx4 are tWP,dr, 192304.9,-0.0005, 192304.9,0.0013, 192304.9,-0.045, 192304.9,0.0014\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2858 2 149649.3314142204 2.4723\n",
      "loop32.0, tr2858,wedgePops755095.7, 192586.6, 191898.6, 178543.5, 192067.1, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?10120 \n",
      "   targetWP, latest drx4 are tWP,dr, 192304.9,-0.0005, 192304.9,0.0013, 192304.9,0.0225, 192304.9,0.0014\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2858 2 178543.47764979408 2.4947\n",
      "loop33.0, tr2858,wedgePops771606.86, 192586.6, 191898.6, 195054.6, 192067.1, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?10120 \n",
      "   targetWP, latest drx4 are tWP,dr, 192304.9,-0.0005, 192304.9,0.0013, 192304.9,0.0112, 192304.9,0.0014\n",
      "I am working on tract number 2860 of 5160 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2865\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2870 4.0 3 49.5 0.5129 248949.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2870 5.0 3 49.5 0.4785 248949.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2879 4.0 0 138.0 0.2257 45451.7\n",
      "I am working on tract number 2880 of 5160 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2880\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2889 5.0 0 130.5 0.1879 70035.6\n",
      "we have 2 non-opposing shorted wedges for tract no 2890\n",
      "I am working on tract number 2900 of 5160 tracts\n",
      "I am working on tract number 2920 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2928 4.0 0 137.0 0.268 43102.3\n",
      "I am working on tract number 2940 of 5160 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2951 2 388668.93831178 0.7261\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2951 2 94648.77660494595 0.3631\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2951 2 397521.6772797144 0.7577\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2951 2 221258.48337033045 0.5604\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2951 2 106759.56361952123 0.4617\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2951 2 136327.8581776892 0.511\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2951 2 176709.3422050412 0.5357\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2951 2 201562.75885857648 0.548\n",
      "I am working on tract number 2960 of 5160 tracts\n",
      "I am working on tract number 2980 of 5160 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2993 1 258626.08415044937 1.2349\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2993 1 13492.472372196207 0.6175\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2993 1 293295.8189605208 1.2585\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2993 1 26943.722194397356 0.938\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2993 1 63215.17504769212 1.0982\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2993 1 159503.33861992627 1.1784\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2993 1 225785.06224306207 1.2184\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2993 1 264340.94519821723 1.2384\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2993 1 246697.48470160074 1.2284\n",
      "I am working on tract number 3000 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3015 4.0 0 136.3 0.2407 47932.6\n",
      "I am working on tract number 3020 of 5160 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3020 1 397923.3818724537 1.8044\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3020 1 44305.23149836302 0.9022\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3020 1 444568.6835372582 2.2458\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3020 1 246594.00865757227 1.574\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3020 1 59251.65322159184 1.2381\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3020 1 78813.95627290875 1.4061\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3020 1 123824.1737924472 1.49\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3020 1 184066.1967449237 1.532\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3020 1 213316.43776462544 1.553\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3022 1 245929.6558070298 1.5797\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3022 1 24519.951130288304 0.7899\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3022 1 421378.26361281576 2.1927\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3022 1 115328.32320645847 1.4913\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3022 1 385725.02008437365 1.842\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3022 1 352321.32360141136 1.6666\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3022 1 244322.00985705768 1.5789\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3022 1 178492.46365407325 1.5351\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3022 1 206364.7510758602 1.557\n",
      "13 yoyos for tract,wedge,wedgePop,r= 3022 1 222562.38371753952 1.568\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3023 1 425890.77072515746 2.2961\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3023 1 43377.389195294934 1.1481\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3023 1 454325.80839632807 2.6389\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3023 1 375544.2849316548 1.8935\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3023 1 64167.88842177618 1.5208\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3023 1 175339.00518587852 1.7071\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3023 1 317612.91900457407 1.8003\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3023 1 236809.70891300467 1.7537\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3023 1 205001.1879037043 1.7304\n",
      "13 yoyos for tract,wedge,wedgePop,r= 3023 1 219470.69783871615 1.7421\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3035 5.0 0 131.9 0.331 67695.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3035 6.0 0 131.9 0.6712 67695.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3039 4.0 0 139.3 0.5142 50931.4\n",
      "I am working on tract number 3040 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3041 4.0 0 143.6 0.9761 5281.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3045 2.0 2 90.0 0.2939 46281.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3045 6.0 0 126.1 0.7371 83598.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3046 6.0 0 132.8 0.6154 71780.5\n",
      "I am working on tract number 3060 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3068 6.0 0 127.5 0.4226 75437.7\n",
      "I am working on tract number 3080 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3095 2.0 3 90.0 0.2339 5096.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3097 2.0 3 90.0 0.1376 109421.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3097 5.0 0 109.0 0.1338 125071.3\n",
      "I am working on tract number 3100 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3102 3.0 0 90.0 0.1798 116604.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3105 8.0 0 83.3 0.6638 255034.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3105 9.0 0 83.3 0.6542 255034.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3105 10.0 0 83.3 0.6447 255034.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3105 11.0 0 83.3 0.6354 255034.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3105 12.0 0 83.3 0.6262 255034.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3105 13.0 0 83.3 0.6171 255034.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3105 14.0 0 83.3 0.6082 255034.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3105 15.0 0 83.3 0.5994 255034.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3112 4.0 0 90.0 0.4053 103290.3\n",
      "I am working on tract number 3120 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3120 6.0 1 65.1 1.4897 84068.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3129 2.0 2 90.0 0.7542 183993.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3129 3.0 2 90.0 0.7795 183993.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3129 4.0 2 90.0 0.8056 183993.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3132 5.0 0 143.8 1.6395 87372.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3139 4.0 0 129.7 0.2971 64640.5\n",
      "I am working on tract number 3140 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3140 2.0 3 90.0 0.333 772797.0\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3148 1 1556720.0894527442 0.9094\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3148 1 14633.976487218635 0.4547\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3148 1 1589367.799589994 1.0356\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3148 1 861752.7674081885 0.7451\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3148 1 182352.11763665115 0.5999\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3148 1 470591.91615698073 0.6725\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3148 1 317536.7383276733 0.6362\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3148 1 248288.4480067934 0.6181\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3148 1 214304.5221553108 0.609\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3148 1 197824.34984518244 0.6045\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3151 4.0 0 123.5 0.2289 89842.0\n",
      "I am working on tract number 3160 of 5160 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3160\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3164 2.0 1 90.0 0.5681 93767.6\n",
      "we have 2 non-opposing shorted wedges for tract no 3164\n",
      "we have 2 non-opposing shorted wedges for tract no 3166\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3167 6.0 1 37.8 2.1782 73879.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3173 2.0 2 90.0 0.6454 276292.6\n",
      "I am working on tract number 3180 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3183 4.0 0 120.5 0.3941 110035.2\n",
      "I am working on tract number 3200 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3202 2.0 3 90.0 0.5861 10792.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3203 8.0 3 42.7 0.8327 340521.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3205 4.0 0 143.8 1.1444 1151.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3207 8.0 0 139.7 0.9405 51826.9\n",
      "I am working on tract number 3220 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3234 2.0 3 90.0 0.4154 10273.1\n",
      "I am working on tract number 3240 of 5160 tracts\n",
      "I am working on tract number 3260 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3271 4.0 0 139.9 0.3865 23648.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3279 5.0 0 135.3 0.2135 51608.0\n",
      "I am working on tract number 3280 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3282 6.0 0 99.2 0.1563 161060.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3282 7.0 0 99.2 0.178 161060.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3282 8.0 0 99.2 0.2027 161060.8\n",
      "I am working on tract number 3300 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3301 7.0 0 102.1 0.3857 147287.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3301 8.0 0 102.1 0.4682 147287.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3301 9.0 0 102.1 0.5684 147287.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3303 8.0 0 93.0 0.3509 177741.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3303 9.0 0 93.0 0.372 177741.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3303 10.0 0 93.0 0.3944 177741.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3303 11.0 0 93.0 0.4182 177741.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3303 12.0 0 93.0 0.4434 177741.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3303 13.0 0 93.0 0.4701 177741.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3303 14.0 0 93.0 0.4984 177741.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3303 15.0 0 93.0 0.5284 177741.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3303 16.0 0 93.0 0.5602 177741.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3303 17.0 0 93.0 0.594 177741.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3303 18.0 0 93.0 0.6298 177741.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3303 19.0 0 93.0 0.6677 177741.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3306 5.0 0 138.1 0.4551 41030.4\n",
      "I am working on tract number 3320 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3321 2.0 3 90.0 0.4119 21050.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3321 5.0 0 135.3 0.3502 44287.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3322 5.0 0 132.0 0.4396 53937.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3326 4.0 0 142.2 0.7631 10885.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3329 2.0 1 90.0 0.6282 292928.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3329 3.0 1 90.0 0.4494 292928.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3333 4.0 0 139.7 0.6724 25792.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3334 7.0 0 133.8 0.3714 53726.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3337 2.0 2 90.0 0.6652 10560.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3337 5.0 0 141.5 0.6657 17448.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3338 4.0 0 135.8 0.41 40282.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3339 2.0 2 90.0 0.3894 30947.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3339 5.0 0 130.4 0.3263 62308.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3339 6.0 0 130.4 0.6967 62308.8\n",
      "I am working on tract number 3340 of 5160 tracts\n",
      "I am working on tract number 3360 of 5160 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3375\n",
      "I am working on tract number 3380 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3384 4.0 0 130.4 0.6504 59302.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3388 4.0 0 126.0 0.1829 86264.7\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3389 1 307649.60761398805 0.8901\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3389 1 95175.85650518115 0.4451\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3389 1 308599.8189147363 0.9071\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3389 1 238622.49576117855 0.6761\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3389 1 113396.16375842175 0.5606\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3389 1 165628.286058025 0.6183\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3389 1 205191.61905667785 0.6472\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3389 1 186040.97690286816 0.6328\n",
      "I am working on tract number 3400 of 5160 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3406\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3418 4.0 0 132.5 0.2063 63077.2\n",
      "I am working on tract number 3420 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3426 5.0 0 90.0 0.3472 154456.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3431 2.0 2 90.0 0.196 66958.7\n",
      "we have 2 non-opposing shorted wedges for tract no 3432\n",
      "we have 2 non-opposing shorted wedges for tract no 3433\n",
      "I am working on tract number 3440 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3446 2.0 3 90.0 0.5969 333616.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3448 4.0 0 134.2 0.2559 57365.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3449 14.0 0 91.3 0.177 187558.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3449 15.0 0 91.3 0.1804 187558.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3449 16.0 0 91.3 0.1838 187558.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3449 17.0 0 91.3 0.1872 187558.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3449 18.0 0 91.3 0.1908 187558.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3449 19.0 0 91.3 0.1944 187558.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3449 20.0 0 91.3 0.198 187558.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3449 21.0 0 91.3 0.2018 187558.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3449 22.0 0 91.3 0.2056 187558.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3449 23.0 0 91.3 0.2095 187558.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3449 24.0 0 91.3 0.2134 187558.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3449 25.0 0 91.3 0.2174 187558.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3449 26.0 0 91.3 0.2215 187558.6\n",
      "I am working on tract number 3460 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3461 4.0 0 139.3 0.2527 28954.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3473 4.0 0 137.8 0.2529 41199.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3474 7.0 0 116.6 0.2043 119243.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3475 3.0 0 131.1 0.2908 89538.2\n",
      "I am working on tract number 3480 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3480 4.0 0 135.0 0.2598 66405.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3484 5.0 1 90.0 0.2825 166560.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3484 6.0 1 90.0 0.3141 166560.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3490 4.0 0 131.2 0.2634 52766.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3492 2.0 0 90.0 1.4842 1814.8\n",
      "I am working on tract number 3500 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3501 6.0 0 138.3 0.8766 41641.0\n",
      "I am working on tract number 3520 of 5160 tracts\n",
      "I am working on tract number 3540 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3555 2.0 0 90.0 0.7374 197101.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3555 3.0 0 90.0 0.7239 197101.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3555 4.0 0 90.0 0.7106 197101.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3555 5.0 0 90.0 0.6975 197101.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3555 6.0 0 90.0 0.6847 197101.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3555 7.0 0 90.0 0.6722 197101.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3555 8.0 0 90.0 0.6598 197101.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3555 9.0 0 90.0 0.6477 197101.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3555 10.0 0 90.0 0.6358 197101.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3555 11.0 0 90.0 0.6241 197101.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3555 12.0 0 90.0 0.6127 197101.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3555 13.0 0 90.0 0.6014 197101.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3555 14.0 0 90.0 0.5904 197101.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3555 15.0 0 90.0 0.5795 197101.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3555 16.0 0 90.0 0.5689 197101.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3555 17.0 0 90.0 0.5584 197101.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3558 2.0 0 90.0 0.6361 216891.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3558 3.0 0 90.0 0.5804 216891.9\n",
      "I am working on tract number 3560 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3572 3.0 0 90.0 0.7044 189843.2\n",
      "I am working on tract number 3580 of 5160 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3582\n",
      "I am working on tract number 3600 of 5160 tracts\n",
      "I am working on tract number 3620 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3625 4.0 0 141.7 0.1953 19345.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3626 7.0 0 113.0 0.3076 286009.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3636 9.0 1 68.9 0.2839 183446.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3636 10.0 1 68.9 0.3219 183446.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3636 11.0 1 68.9 0.3649 183446.4\n",
      "I am working on tract number 3640 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3655 8.0 3 64.0 1.1713 140320.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3658 2.0 2 90.0 0.4433 25728.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3658 5.0 1 46.4 0.2314 165946.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3658 5.0 3 46.4 0.3092 83019.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3658 6.0 1 46.4 0.258 165946.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3659 4.0 3 38.6 0.4127 63149.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3659 5.0 0 141.4 1.3655 44644.5\n",
      "I am working on tract number 3660 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3667 3.0 0 90.0 0.3085 89613.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3667 4.0 0 90.0 0.5237 89613.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3667 4.0 3 90.0 1.0644 81232.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3668 4.0 1 90.0 0.5791 101626.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3669 4.0 2 90.0 0.8328 102066.9\n",
      "we have 2 OPPOSING shorted wedges for tract no 3678\n",
      "I am working on tract number 3680 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3689 2.0 0 90.0 0.3528 21075.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3689 5.0 3 42.2 0.2716 58352.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3690 4.0 3 37.5 0.3494 31442.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3691 2.0 2 90.0 0.2352 31101.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3692 10.0 0 142.4 2.5565 242739.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3692 11.0 0 142.4 2.6254 242739.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3693 2.0 1 90.0 0.2016 40993.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3694 2.0 1 90.0 0.2597 34893.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3694 5.0 3 49.8 0.2884 37251.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3695 2.0 1 90.0 0.3281 20559.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3695 5.0 3 44.3 0.3672 23719.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3696 2.0 2 90.0 0.2027 47998.0\n",
      "I am working on tract number 3700 of 5160 tracts\n",
      "I am working on tract number 3720 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3722 3.0 0 90.0 0.8626 237559.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3722 4.0 0 90.0 0.7329 237559.0\n",
      "I am working on tract number 3740 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3758 4.0 2 90.0 0.2612 150839.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3758 5.0 2 90.0 0.3118 150839.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3758 6.0 2 90.0 0.3721 150839.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3758 7.0 2 90.0 0.4442 150839.1\n",
      "I am working on tract number 3760 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3768 4.0 0 90.0 0.3495 148068.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3768 5.0 0 90.0 0.4227 148068.3\n",
      "I am working on tract number 3780 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3793 3.0 0 138.7 0.8928 124084.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3793 4.0 0 138.7 1.2208 124084.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3794 3.0 1 90.0 0.57 158262.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3794 4.0 1 90.0 0.6575 158262.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3794 5.0 1 90.0 0.7584 158262.0\n",
      "I am working on tract number 3800 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3802 5.0 0 133.2 0.9809 55121.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3814 13.0 3 40.3 1.6522 457528.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3815 2.0 3 90.0 0.1481 223195.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3815 3.0 3 90.0 0.1321 223195.1\n",
      "I am working on tract number 3820 of 5160 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3825\n",
      "I am working on tract number 3840 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3857 12.0 1 57.6 0.4424 242320.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3857 13.0 1 57.6 0.4153 242320.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3857 14.0 1 57.6 0.3898 242320.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3857 15.0 1 57.6 0.3658 242320.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3857 16.0 1 57.6 0.3433 242320.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3857 17.0 1 57.6 0.3223 242320.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3857 18.0 1 57.6 0.3025 242320.7\n",
      "I am working on tract number 3860 of 5160 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3861\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3877 4.0 0 137.3 0.3965 28773.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3878 4.0 3 42.4 0.4758 15695.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3879 4.0 3 39.2 0.4272 15002.4\n",
      "I am working on tract number 3880 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3888 4.0 1 33.9 0.3292 35794.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3888 5.0 0 146.1 1.1566 27211.4\n",
      "I am working on tract number 3900 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3905 3.0 3 90.0 0.2968 72894.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3905 6.0 0 88.9 0.2284 19798.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3906 5.0 0 92.7 0.2472 23548.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3908 2.0 2 90.0 0.2407 39391.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3908 5.0 0 93.6 0.2365 41029.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3909 5.0 0 87.0 0.2293 51377.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3912 2.0 2 90.0 0.422 10485.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3912 3.0 2 90.0 1.1593 10485.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3913 3.0 1 90.0 1.0589 17741.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3913 8.0 0 136.1 0.416 70309.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3914 2.0 3 90.0 0.2073 48695.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3914 5.0 0 95.2 0.2555 30730.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3916 7.0 0 101.0 0.451 121319.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3917 3.0 1 90.0 0.2689 111262.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3917 4.0 1 90.0 0.3959 111262.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3917 7.0 0 103.3 0.1909 72926.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3917 8.0 0 103.3 0.3695 72926.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3918 3.0 2 90.0 0.4364 70603.4\n",
      "I am working on tract number 3920 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3922 5.0 0 83.9 0.4018 23385.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3924 4.0 0 143.3 0.5073 42866.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3938 4.0 0 135.3 0.1995 47957.1\n",
      "I am working on tract number 3940 of 5160 tracts\n",
      "I am working on tract number 3960 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3964 2.0 0 90.0 0.5256 470090.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3964 3.0 0 90.0 0.2628 470090.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3969 7.0 0 127.9 0.7481 183263.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3969 8.0 0 127.9 0.8677 183263.4\n",
      "I am working on tract number 3980 of 5160 tracts\n",
      "I am working on tract number 4000 of 5160 tracts\n",
      "I am working on tract number 4020 of 5160 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4024\n",
      "I am working on tract number 4040 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4042 2.0 3 90.0 0.3408 19157.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4044 11.0 3 38.6 1.3022 229964.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4044 12.0 3 38.6 1.3615 229964.0\n",
      "I am working on tract number 4060 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4060 5.0 0 131.6 0.3812 242173.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4060 6.0 0 131.6 0.3675 242173.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4060 7.0 0 131.6 0.3543 242173.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4060 8.0 0 131.6 0.3415 242173.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4061 6.0 0 110.5 0.1289 127941.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4061 7.0 0 110.5 0.1726 127941.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4068 9.0 0 96.4 0.1485 179465.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4068 10.0 0 96.4 0.1563 179465.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4068 11.0 0 96.4 0.1646 179465.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4068 12.0 0 96.4 0.1733 179465.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4068 13.0 0 96.4 0.1824 179465.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4068 14.0 0 96.4 0.192 179465.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4068 15.0 0 96.4 0.2022 179465.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4068 16.0 0 96.4 0.2128 179465.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4068 17.0 0 96.4 0.224 179465.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4076 4.0 1 90.0 0.5272 129742.1\n",
      "I am working on tract number 4080 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 7.0 0 90.0 0.2567 188071.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 8.0 0 90.0 0.261 188071.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 9.0 0 90.0 0.2654 188071.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 10.0 0 90.0 0.2698 188071.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 11.0 0 90.0 0.2744 188071.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 12.0 0 90.0 0.279 188071.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 13.0 0 90.0 0.2837 188071.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 14.0 0 90.0 0.2884 188071.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 15.0 0 90.0 0.2933 188071.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 16.0 0 90.0 0.2982 188071.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 17.0 0 90.0 0.3032 188071.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 18.0 0 90.0 0.3083 188071.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 19.0 0 90.0 0.3135 188071.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 20.0 0 90.0 0.3187 188071.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 21.0 0 90.0 0.3241 188071.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 22.0 0 90.0 0.3295 188071.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 23.0 0 90.0 0.3351 188071.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 24.0 0 90.0 0.3407 188071.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 25.0 0 90.0 0.3464 188071.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 26.0 0 90.0 0.3522 188071.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 27.0 0 90.0 0.3581 188071.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 28.0 0 90.0 0.3642 188071.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 29.0 0 90.0 0.3703 188071.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 30.0 0 90.0 0.3765 188071.0\n",
      "loop31.0, tr4081,wedgePops764874.91, 188071.0, 192488.7, 192363.5, 191951.7, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?0120 \n",
      "   targetWP, latest drx4 are tWP,dr, 192304.9,0.0063, 192304.9,-0.0005, 192304.9,0.0005, 192304.9,0.0009\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 31.0 0 90.0 0.3828 188071.0\n",
      "loop32.0, tr4081,wedgePops764874.91, 188071.0, 192488.7, 192363.5, 191951.7, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?0120 \n",
      "   targetWP, latest drx4 are tWP,dr, 192304.9,0.0064, 192304.9,-0.0005, 192304.9,0.0005, 192304.9,0.0009\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 32.0 0 90.0 0.3892 188071.0\n",
      "loop33.0, tr4081,wedgePops764874.91, 188071.0, 192488.7, 192363.5, 191951.7, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?0120 \n",
      "   targetWP, latest drx4 are tWP,dr, 192304.9,0.0065, 192304.9,-0.0005, 192304.9,0.0005, 192304.9,0.0009\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 33.0 0 90.0 0.3958 188071.0\n",
      "loop34.0, tr4081,wedgePops764874.91, 188071.0, 192488.7, 192363.5, 191951.7, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?0120 \n",
      "   targetWP, latest drx4 are tWP,dr, 192304.9,0.0066, 192304.9,-0.0005, 192304.9,0.0005, 192304.9,0.0009\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 34.0 0 90.0 0.4024 188071.0\n",
      "loop35.0, tr4081,wedgePops764874.91, 188071.0, 192488.7, 192363.5, 191951.7, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?0120 \n",
      "   targetWP, latest drx4 are tWP,dr, 192304.9,0.0068, 192304.9,-0.0005, 192304.9,0.0005, 192304.9,0.0009\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 35.0 0 90.0 0.4092 188071.0\n",
      "loop36.0, tr4081,wedgePops764874.91, 188071.0, 192488.7, 192363.5, 191951.7, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?0120 \n",
      "   targetWP, latest drx4 are tWP,dr, 192304.9,0.0069, 192304.9,-0.0005, 192304.9,0.0005, 192304.9,0.0009\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 36.0 0 90.0 0.416 188071.0\n",
      "loop37.0, tr4081,wedgePops764874.91, 188071.0, 192488.7, 192363.5, 191951.7, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?0120 \n",
      "   targetWP, latest drx4 are tWP,dr, 192304.9,0.007, 192304.9,-0.0005, 192304.9,0.0005, 192304.9,0.0009\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 37.0 0 90.0 0.423 188071.0\n",
      "loop38.0, tr4081,wedgePops764874.91, 188071.0, 192488.7, 192363.5, 191951.7, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?0120 \n",
      "   targetWP, latest drx4 are tWP,dr, 192304.9,0.0071, 192304.9,-0.0005, 192304.9,0.0005, 192304.9,0.0009\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 38.0 0 90.0 0.4301 188071.0\n",
      "loop39.0, tr4081,wedgePops764874.91, 188071.0, 192488.7, 192363.5, 191951.7, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?0120 \n",
      "   targetWP, latest drx4 are tWP,dr, 192304.9,0.0072, 192304.9,-0.0005, 192304.9,0.0005, 192304.9,0.0009\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 0.43735 188070.9804 188070.9804 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 0.4094 193353.0139 192488.7256 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.27629 191472.9356 192363.5284 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 0.18132 190568.1585 191951.6731 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4081 39.0 0 90.0 0.4374 188071.0\n",
      "loop40.0, tr4081,wedgePops764874.91, 188071.0, 192488.7, 192363.5, 191951.7, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?0120 \n",
      "   targetWP, latest drx4 are tWP,dr, 192304.9,0.0073, 192304.9,-0.0005, 192304.9,0.0005, 192304.9,0.0009\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 0.44469 188070.9804 188070.9804 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 0.4094 193353.0139 192488.7256 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.27629 191472.9356 192363.5284 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 0.18132 190568.1585 191951.6731 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 4081, giving up w pop 764874.9075254685\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4082 4.0 1 90.0 0.4438 118206.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4084 8.0 0 90.0 0.7732 191263.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4095 4.0 1 45.5 0.3544 62900.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4095 5.0 1 45.5 0.7524 62900.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4096 6.0 0 89.3 0.2631 22818.2\n",
      "I am working on tract number 4100 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4105 3.0 0 90.0 1.0082 57533.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4106 3.0 2 90.0 1.1139 19565.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4107 2.0 2 90.0 0.3695 18610.0\n",
      "I am working on tract number 4120 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4128 8.0 0 108.8 0.6607 130092.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4134 4.0 0 132.0 0.2558 63120.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4139 8.0 0 111.6 0.3944 121476.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4139 9.0 0 111.6 0.5472 121476.9\n",
      "I am working on tract number 4140 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4142 8.0 0 122.8 0.2354 101488.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4146 2.0 3 90.0 0.367 20860.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4146 6.0 0 135.6 0.8598 49756.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4147 3.0 0 90.0 1.0697 27599.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4149 2.0 1 90.0 0.3351 30114.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4149 7.0 0 131.6 1.3694 69243.3\n",
      "I am working on tract number 4160 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4165 5.0 0 100.4 0.2109 161853.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4165 6.0 0 100.4 0.2393 161853.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4166 7.0 0 107.5 0.1739 146573.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4166 8.0 0 107.5 0.2118 146573.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4173 2.0 2 90.0 0.2796 142891.6\n",
      "I am working on tract number 4180 of 5160 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4187\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4188 4.0 0 124.9 0.2718 76041.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4195 2.0 3 90.0 0.3295 76120.4\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4195 3 224773.12313505285 0.3969\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4195 3 135493.8879109774 0.1984\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4195 3 717489.5537393645 0.6174\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4195 3 247343.85251759348 0.4079\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4195 3 141182.94112103464 0.3032\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4195 3 161844.29875314882 0.3555\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4195 3 191420.45731054415 0.3817\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4195 3 220382.63316311396 0.3948\n",
      "I am working on tract number 4200 of 5160 tracts\n",
      "I am working on tract number 4220 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4226 6.0 0 138.0 1.1251 43153.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4227 6.0 0 139.0 1.2124 65993.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4229 8.0 0 117.8 0.4829 111225.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4229 9.0 0 117.8 0.711 111225.3\n",
      "I am working on tract number 4240 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4242 3.0 1 90.0 0.317 130797.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4242 4.0 1 90.0 0.4181 130797.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4253 6.0 1 71.2 1.9873 84962.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4254 4.0 0 143.6 1.0094 1803.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4258 7.0 0 125.7 1.2217 102484.2\n",
      "I am working on tract number 4260 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4265 7.0 0 142.6 1.2917 171054.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4266 6.0 0 140.9 1.1294 183899.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4266 7.0 0 140.9 1.1677 183899.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4266 8.0 0 140.9 1.2073 183899.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4266 9.0 0 140.9 1.2482 183899.2\n",
      "we have 2 OPPOSING shorted wedges for tract no 4267\n",
      "we have 2 OPPOSING shorted wedges for tract no 4268\n",
      "we have 2 OPPOSING shorted wedges for tract no 4271\n",
      "I am working on tract number 4280 of 5160 tracts\n",
      "I am working on tract number 4300 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4313 8.0 0 107.0 0.4197 149926.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4313 9.0 0 107.0 0.5032 149926.1\n",
      "I am working on tract number 4320 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4328 3.0 2 90.0 0.3864 73091.3\n",
      "I am working on tract number 4340 of 5160 tracts\n",
      "I am working on tract number 4360 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4376 4.0 0 139.2 0.3647 24934.4\n",
      "I am working on tract number 4380 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4383 6.0 3 90.0 1.4992 160801.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4383 7.0 3 90.0 1.7097 160801.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4383 8.0 3 90.0 1.9497 160801.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4383 9.0 3 90.0 2.2234 160801.1\n",
      "I am working on tract number 4400 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4405 5.0 0 141.8 0.7571 10847.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4408 4.0 0 142.7 0.8833 11130.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4410 7.0 0 86.5 0.2704 202081.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4410 8.0 0 86.5 0.2605 202081.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4410 9.0 0 86.5 0.2509 202081.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4410 10.0 0 86.5 0.2417 202081.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4410 11.0 0 86.5 0.2328 202081.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4410 12.0 0 86.5 0.2243 202081.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4410 13.0 0 86.5 0.216 202081.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4410 14.0 0 86.5 0.2081 202081.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4417 2.0 0 90.0 0.1771 66063.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4417 5.0 0 119.2 0.1577 101296.4\n",
      "I am working on tract number 4420 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4422 8.0 0 99.8 0.1596 160062.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4422 9.0 0 99.8 0.1826 160062.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4422 10.0 0 99.8 0.2089 160062.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4427 2.0 2 90.0 0.304 26540.6\n",
      "we have 2 non-opposing shorted wedges for tract no 4427\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4429 5.0 0 117.8 0.3613 149880.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4438 7.0 0 117.6 0.1849 106415.6\n",
      "I am working on tract number 4440 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4449 2.0 0 90.0 0.2702 32795.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4451 4.0 0 138.6 0.2493 39533.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4459 3.0 3 90.0 0.3946 83533.1\n",
      "I am working on tract number 4460 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4465 2.0 0 90.0 0.6818 237135.9\n",
      "I am working on tract number 4480 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4488 5.0 0 121.4 0.4051 84470.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4488 6.0 0 121.4 0.7143 84470.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4490 5.0 0 106.3 0.2897 155125.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4490 6.0 0 106.3 0.339 155125.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4490 7.0 0 106.3 0.3966 155125.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4490 8.0 0 106.3 0.4641 155125.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4490 9.0 0 106.3 0.543 155125.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4492 5.0 0 140.8 0.4917 17909.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4493 9.0 0 109.6 0.3946 136707.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4493 10.0 0 109.6 0.5047 136707.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4495 7.0 0 118.6 0.6912 117184.7\n",
      "I am working on tract number 4500 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4502 8.0 0 132.7 0.4309 150490.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4502 9.0 0 132.7 0.5151 150490.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4502 10.0 0 132.7 0.6159 150490.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4502 11.0 0 132.7 0.7364 150490.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4505 6.0 0 137.7 1.0306 38939.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4506 7.0 0 134.9 0.8086 56038.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4508 9.0 0 127.7 1.0638 94521.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4511 2.0 3 90.0 0.9339 639.5\n",
      "I am working on tract number 4520 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4522 5.0 0 140.3 1.1831 32740.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4523 6.0 0 139.0 1.0869 38289.0\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4525 2 369745.20076534053 1.7326\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4525 2 39932.65127570054 0.8663\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4525 2 366701.2826893757 1.5651\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4525 2 304412.4138621103 1.2157\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4525 2 89594.36857149177 1.041\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4525 2 221640.34956400868 1.1283\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4525 2 144139.3560032407 1.0847\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4525 2 186211.1453078353 1.1065\n",
      "13 yoyos for tract,wedge,wedgePop,r= 4525 2 205065.52515506666 1.1174\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4526 1 349397.788760957 1.7275\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4526 1 18907.019831462763 0.8637\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4526 1 349925.5850850806 1.7312\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4526 1 106170.0500219932 1.2974\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4526 1 322214.43068317685 1.5143\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4526 1 245443.18839818594 1.4059\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4526 1 157733.7620737809 1.3517\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4530 2 367234.49256316543 1.0901\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4530 2 70708.35947321053 0.545\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4530 2 366595.0267510248 1.0874\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4530 2 265065.111666518 0.8162\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4530 2 92321.22387311835 0.6806\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4530 2 175133.48411715485 0.7484\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4530 2 220360.04946668958 0.7823\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4530 2 197695.78423577853 0.7654\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4530 2 186318.5045998381 0.7569\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4531 0 287791.5696458281 0.803\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4531 0 52189.927606376325 0.4015\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4531 0 342865.75799184386 0.8682\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4531 0 86031.63448242602 0.6348\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4531 0 204251.81864867534 0.7515\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4531 0 108622.51980727697 0.6932\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4531 0 150477.8813313801 0.7223\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4531 0 172436.3792530911 0.7369\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4531 0 186887.4730922196 0.7442\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4533 3 390677.6934337657 1.0998\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4533 3 41467.3475615739 0.5499\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4533 3 388841.1749246959 1.0956\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4533 3 265426.68843199953 0.8227\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4533 3 67937.72466983838 0.6863\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4533 3 159236.63113667426 0.7545\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4533 3 223540.86000321936 0.7886\n",
      "I am working on tract number 4540 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4544 5.0 0 142.0 0.5835 13548.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4545 5.0 0 143.4 1.8508 14869.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4548 4.0 0 142.4 0.5065 15633.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4553 4.0 2 435.9 0.6478 93775.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4559 8.0 0 113.7 1.2686 132183.6\n",
      "I am working on tract number 4560 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4562 3.0 1 90.0 0.3362 83369.6\n",
      "we have 2 OPPOSING shorted wedges for tract no 4565\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4569 3.0 3 90.0 0.3996 90484.1\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4569 3 343670.98962323647 2.4237\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4569 3 149636.2333157204 1.2119\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4569 3 343085.0678960886 2.4163\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4569 3 298513.02448922896 1.8141\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4569 3 164205.4101262741 1.513\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4569 3 168575.47405836568 1.6635\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4569 3 187256.66954509736 1.7388\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4569 3 248659.2667422439 1.7764\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4569 3 276398.78402702376 1.7953\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4569 3 263667.23595910927 1.7858\n",
      "loop31.0, tr4569,wedgePops770964.79, 253580.8, 7987.8, 253452.2, 255943.9, Overedge?0, 1, 0, 0, ,Satisfied?1010,yoyo?00011 \n",
      "   targetWP, latest drx4 are tWP,dr, 253743.9,0.0017, 253743.9,0.7373, 253743.9,0.0043, 253743.9,-0.0047\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4570 3.0 1 90.0 0.2431 118706.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4570 4.0 1 90.0 0.3425 118706.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4570 5.0 1 90.0 0.4827 118706.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4571 3.0 0 90.0 0.2911 91785.8\n",
      "I am working on tract number 4580 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4584 4.0 0 93.1 0.3725 13182.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4586 3.0 0 90.0 0.3248 116015.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4586 4.0 0 90.0 0.4649 116015.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4587 2.0 0 90.0 0.295 45938.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4587 5.0 3 52.1 0.2834 65733.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4588 3.0 0 90.0 0.3798 130215.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4589 4.0 0 140.1 0.4266 79937.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4593 2.0 1 90.0 0.2317 36367.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4593 5.0 3 44.8 0.2703 38027.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4597 4.0 0 129.0 0.39 76047.2\n",
      "I am working on tract number 4600 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4607 2.0 2 90.0 0.2085 46210.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4607 5.0 0 94.2 0.2051 48079.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4608 4.0 0 143.8 0.6922 16995.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4609 3.0 3 90.0 0.3537 81670.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4610 2.0 1 90.0 0.3105 22016.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4610 5.0 0 83.5 0.3288 19579.0\n",
      "I am working on tract number 4620 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4621 7.0 0 124.9 0.3652 144636.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4621 8.0 0 124.9 0.4491 144636.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4621 9.0 0 124.9 0.5522 144636.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4623 7.0 0 129.3 0.4183 123361.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4623 8.0 0 129.3 0.5743 123361.3\n",
      "we have 2 OPPOSING shorted wedges for tract no 4627\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4636 7.0 0 95.8 0.361 167664.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4636 8.0 0 95.8 0.3994 167664.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4636 9.0 0 95.8 0.4419 167664.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4636 10.0 0 95.8 0.489 167664.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4636 11.0 0 95.8 0.541 167664.8\n",
      "I am working on tract number 4640 of 5160 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4646\n",
      "we have 2 non-opposing shorted wedges for tract no 4650\n",
      "I am working on tract number 4660 of 5160 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4670\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4676 2.0 1 90.0 0.468 36008.0\n",
      "I am working on tract number 4680 of 5160 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4684 1 445125.4672480712 2.3839\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4684 1 56429.16877446271 1.192\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4684 1 481373.0078532278 2.7004\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4684 1 392050.29489818786 1.9462\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4684 1 74244.45830727584 1.5691\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4684 1 152710.68941737554 1.7576\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4684 1 317462.84269155096 1.8519\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4684 1 227474.76598053635 1.8048\n",
      "13 yoyos for tract,wedge,wedgePop,r= 4684 1 270042.98513856996 1.8284\n",
      "we have 2 OPPOSING shorted wedges for tract no 4685\n",
      "I am working on tract number 4700 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4701 2.0 0 90.0 0.3586 21626.7\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4710 1 417210.7280404271 2.143\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4710 1 35747.81660976651 1.0715\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4710 1 422268.6853346444 2.2224\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4710 1 280708.3818873819 1.647\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4710 1 49906.190912968916 1.3592\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4710 1 82684.72247832888 1.5031\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4710 1 155941.16803656952 1.575\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4710 1 209251.55897696601 1.611\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4710 1 242064.43590063264 1.629\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4710 1 262259.2767407723 1.638\n",
      "I am working on tract number 4720 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4729 7.0 1 90.0 1.3746 184731.5\n",
      "I am working on tract number 4740 of 5160 tracts\n",
      "I am working on tract number 4760 of 5160 tracts\n",
      "I am working on tract number 4780 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4787 5.0 0 139.5 0.4229 26827.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4797 5.0 0 90.0 2.0235 134336.6\n",
      "I am working on tract number 4800 of 5160 tracts\n",
      "I am working on tract number 4820 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4837 4.0 1 90.0 1.3839 139012.5\n",
      "I am working on tract number 4840 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4854 4.0 1 90.0 0.5647 184370.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4854 5.0 1 90.0 0.5827 184370.1\n",
      "I am working on tract number 4860 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4860 5.0 3 70.4 0.4296 174444.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4868 2.0 2 90.0 0.6705 249541.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4874 7.0 2 92.1 2.2165 37688.3\n",
      "I am working on tract number 4880 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4882 3.0 0 143.1 1.2213 7350.0\n",
      "we have 2 non-opposing shorted wedges for tract no 4894\n",
      "we have 2 non-opposing shorted wedges for tract no 4899\n",
      "I am working on tract number 4900 of 5160 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4902\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4909 6.0 2 405.0 0.3488 55833.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4910 2.0 1 90.0 0.3139 103561.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4910 3.0 1 90.0 0.4846 103561.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4910 4.0 1 90.0 0.7483 103561.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4916 2.0 0 90.0 0.7239 150988.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4916 6.0 0 89.8 0.6551 193261.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4916 7.0 0 89.8 0.6527 193261.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4916 8.0 0 89.8 0.6503 193261.8\n",
      "I am working on tract number 4920 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4936 3.0 0 143.9 1.081 704.2\n",
      "I am working on tract number 4940 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4940 2.0 0 90.0 0.5493 5199.7\n",
      "I am working on tract number 4960 of 5160 tracts\n",
      "I am working on tract number 4980 of 5160 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4995 1 521873.52560289646 4.7937\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4995 1 28046.979938563134 2.3968\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4995 1 496903.38073731714 4.726\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4995 1 377635.2910274858 3.5614\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4995 1 47108.33906657167 2.9791\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4995 1 134457.64338093775 3.2703\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4995 1 335186.1230762332 3.4158\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4995 1 234545.98003088374 3.343\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4995 1 182900.38007724937 3.3067\n",
      "13 yoyos for tract,wedge,wedgePop,r= 4995 1 209335.2817814396 3.3248\n",
      "13 yoyos for tract,wedge,wedgePop,r= 4995 1 195938.4625687987 3.3157\n",
      "I am working on tract number 5000 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5008 4.0 0 90.0 0.5849 138424.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5008 5.0 0 90.0 0.7416 138424.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5010 11.0 0 361.9 3.6126 713626.6\n",
      "I am working on tract number 5020 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5029 3.0 0 143.9 0.963 586.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5033 4.0 0 123.0 0.2245 88158.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5038 5.0 0 134.8 0.2665 51476.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5039 6.0 0 105.7 0.1887 158612.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5039 7.0 0 105.7 0.2173 158612.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5039 8.0 0 105.7 0.2503 158612.4\n",
      "I am working on tract number 5040 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5054 4.0 0 134.0 0.1932 56368.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5057 2.0 0 90.0 0.4893 119688.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5057 5.0 0 107.8 0.432 171984.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5057 6.0 0 107.8 0.4692 171984.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5057 7.0 0 107.8 0.5096 171984.5\n",
      "I am working on tract number 5060 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5060 2.0 2 90.0 0.8997 686.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5061 4.0 0 143.7 0.9489 1573.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5062 8.0 0 138.4 1.2296 25450.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5063 6.0 0 140.6 0.2584 25078.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5064 8.0 3 90.0 0.3447 188255.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5064 9.0 3 90.0 0.3503 188255.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5064 10.0 3 90.0 0.3559 188255.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5070 4.0 0 138.8 0.2579 35417.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5075 2.0 2 90.0 0.2179 31245.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5075 5.0 0 135.9 0.2169 47242.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5078 2.0 2 90.0 0.2077 71760.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5078 5.0 0 119.0 0.1817 114812.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5078 6.0 0 119.0 0.2619 114812.6\n",
      "I am working on tract number 5080 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5086 6.0 0 132.7 0.2146 61506.0\n",
      "I am working on tract number 5100 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5101 2.0 3 90.0 0.1683 36905.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5102 12.0 0 94.3 0.1963 175943.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5102 13.0 0 94.3 0.2097 175943.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5108 6.0 0 131.7 0.241 73669.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5109 4.0 0 90.0 0.2574 174574.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5109 5.0 0 90.0 0.2765 174574.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5109 6.0 0 90.0 0.2971 174574.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5109 9.0 0 99.2 0.1614 161338.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5109 10.0 0 99.2 0.1837 161338.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5109 11.0 0 99.2 0.2089 161338.7\n",
      "I am working on tract number 5120 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5130 4.0 0 135.1 0.2126 51719.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5131 10.0 0 126.4 0.2053 83322.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5132 5.0 0 118.6 0.1882 124609.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5132 6.0 0 118.6 0.2567 124609.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5133 5.0 0 118.3 0.1754 124196.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5133 6.0 0 118.3 0.2397 124196.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5138 2.0 1 90.0 0.2046 59237.1\n",
      "I am working on tract number 5140 of 5160 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5150 5.0 0 113.5 0.178 131883.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5150 6.0 0 113.5 0.2334 131883.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5152 2.0 0 90.0 0.2619 26202.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5159 2.0 3 90.0 0.1669 42877.7\n",
      "Sum of weights over all precinct home districts should have been 1.000, but was  1.0003391221945768\n",
      "Average and max number of wedgePop loops per tract were:  7.287209302325581 40.0\n",
      "min,average,max of Home District areas were:  0.0 0.6129764281638655 4.027851352404535\n",
      "calculated statewide vote was 0.5067292903108906, should have been 0.5169475466214156\n",
      "calcd statewide Hispanic pop was 0.2528435129292037, should have been 0.24754458559640935\n",
      "calcd statewide Black pop was 0.138035166410581, should have been 0.13519624167898556\n",
      "fraction of HDs that with altered wedge angles near boundaries =  0.38410852713178295\n"
     ]
    }
   ],
   "source": [
    "#3/13/22 - from HD2, but using coeff1, coeff2 (unrestricted opp wedge pop, wideAngle quadratic in ratio)\n",
    "# Bisect for yoyo > 3, precinct calcs after wedges finalized.  More aggressive Euler gain\n",
    "#minTractPop = 10  #this is set in a prior block\n",
    "pi=3.1415926536\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2*pi / nWedges\n",
    "wedgeTriAR = [math.sin(pi/nWedges)*math.cos(pi/nWedges)]*nWedges  #wedge triangle area ratio (= area / r*r)\n",
    "angle = [0.]*nWedges\n",
    "angle2 = [0.]*nWedges\n",
    "pt1 = [Point(0,0)]*nWedges  #these four define the polygon wedge for a growing home district\n",
    "pt2 = [Point(0,0)]*nWedges\n",
    "pt3 = [Point(0,0)]*nWedges\n",
    "pt4 = [Point(0,0)]*nWedges\n",
    "oldR = [0.]*nWedges  #wedge radius from previous loop\n",
    "# guessedR = [0.]*nWedges  #obsolete; was wedge radius if we extrapolate from population density of most recent wedge piece\n",
    "printPoly = [Polygon([(0,0),(0,1),(1,1)])]*nWedges #for debugging\n",
    "wedgePop = [0.]*nWedges\n",
    "tractLoopCounter = [0.]* nTracts  #this tracks how many loops per tract to get to target wedge Pop\n",
    "nearEdge = [0.]* nTracts   #not yet implemented; this will flag tracts near map edge for reprocessing\n",
    "tractUse = [0.] * nTracts  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "precinctUse = [0.] * nPrecincts   #same, but for each precinct / VTD\n",
    "loopTractUse = [0.] * nTracts  #same as above two, but only for the current loop / tract\n",
    "loopPrecinctUse = [0.] * nPrecincts\n",
    "HDvPop = [0.]*nTracts\n",
    "HDvGOP = [0.]*nTracts  #GOP lean of each tract's \"home district\"\n",
    "HDvBlack = [0.]*nTracts\n",
    "HDvHisp = [0.]*nTracts #pct Hispanic by home district\n",
    "HDweight = [0.]*nTracts  #relative weight of each district.  In absence of splits, will equal precinct pop\n",
    "HDarea = [0.]*nTracts  #geographical area of each home district\n",
    "HDradius = [[0.]*nWedges for t in range(nTracts)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nTracts)] #final included angle for each HD wedge\n",
    "angle0 = [-999.] * nTracts  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "toler = 0.005  #adjustable - fractional slop in district population.  normally 0.01, to be reduced to 0.005\n",
    "tolerPop = toler * avgDistrictPop  #absolute slop in district pop\n",
    "nLoopPrint = 30  #at this loop number, we alert user of problem\n",
    "nGiveUp = 40  #we punt after this many loops\n",
    "nLoopSuperPrint = nGiveUp - 2 # at this loop number, we output wedge growth visuals\n",
    "wrongPop = [0.]*nTracts\n",
    "normalGain = 0.8  #adjustable - a bit less than 1.0 for stability, how far we step relative to expected perfect guess\n",
    "EulerGain = 1.5 #if we hit empty land, gain more aggressively to get through it\n",
    "#yoyoFactor = 0.3 #rate of reduction in gain as solver continues to yoyo in solving a wedge  #not currently used ***\n",
    "globalMax_dr = (MAP.area/nDistricts) ** 0.5  #put a reasonable upper limit on wedge radial growth step size\n",
    "tractPrintInterval = 20  #for tracking progress\n",
    "minTractArea = 0.00001 * MAP.area / nTracts  #failsafe for later div-by-zero\n",
    "homePopDensity = avgGridDensity  #seed this\n",
    "didWeRestart = [0] * nTracts  #will flag if we adjusted wedge angles due to a single shorted wedge\n",
    "coeff1 = 0.3  #linear term. we will adjust this empirically to tighten tract weighting near boundaries\n",
    "coeff2 = 0.3  #quadratic term.  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "minWedgePop = [avgDistrictPop/nWedges] * nTracts  #wedge pop for wedge facing boundary (for tracts near boundaries)\n",
    "maxWideAngle = 0.8 * pi  #adjustable parameter.  (avoid wedges too close to half a pie\n",
    "\n",
    "for t in range(nTracts) :  #(nTracts):  #loop on each tract.  Start by resetting stats\n",
    "    gain = normalGain  #in case last precinct had convergence problems\n",
    "    if (t % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on tract number {0} of {1} tracts\".format(t,nTracts) )\n",
    "    tractLoopCounter[t] = 0.  #for stability stats, will track how many times we need to loop for each tract\n",
    "    # isActiveG = [0]*nGrids  #resets whether each grid is relevant to this tract (not used; see alt grid turn-on method)\n",
    "    if (tractArea[t] > minTractArea) :  #too lazy to indent everything.  We'll catch zero tractArea later\n",
    "        homePopDensity = tractPop[t]/tractArea[t]  #temporary - estimate the local density for initial step\n",
    "    tractCP = Point(tractGeom[t].centroid.x,tractGeom[t].centroid.y)  #shorthand for centroid of each tract\n",
    "    for nG in range(nGrids):\n",
    "        if gridGeom[nG].contains(tractCP) :  #which grid contains the centroid of this tract?  Turn it on!\n",
    "            # isActiveG[nG] = 1  #I don't think I use this\n",
    "            homeGridDensity = gridDensity[nG]\n",
    "    maxStartDensity = max(homePopDensity,homeGridDensity)\n",
    "    minR = (avgDistrictPop / maxStartDensity / pi)**0.5   # conservative estimated radius of this tract's district\n",
    "    tinyR = 0.01 * minR   #ensures we start with small, but not infinitesimal wedge populations\n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to our starting wedge\n",
    "    loopTractUse = [0.]*nTracts  #this and below help us reset tract n precinct use after a wedge reset\n",
    "    loopPrecinctUse = [0.]*nPrecincts\n",
    "    # ***GROW WEDGES SIMULTANEOUSLY via ARRAYS, SO WE CAN REACT ON FLY TO BOUNDARY STOPS\n",
    "    HDpop = 0.  #running total of this tract's \"home district\" population\n",
    "    nActiveWedges = nWedges #active wedges have not run over boudnary\n",
    "    targetWedgePop = [avgDistrictPop / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    #latestWedgeDensity = [0.]*nWedges  #stop using # pop'n density of the wedge piece we just added or subtracted\n",
    "    wedgePop = [0.]*nWedges\n",
    "    oldWedgePop = [0.]*nWedges  #wedge pop from previous round (needed for NR projection)\n",
    "    wedgePopGap = [0.]*nWedges\n",
    "    isOverEdge = [0] *nWedges #each wedge is still fully inside map boundary\n",
    "    isSatisfied = [0] * nWedges #wedges close enough to target are not improved upon\n",
    "    yoyoCount = [0] * nWedges #tracks how many times we've reversed this wedge's growth vs. shrink at current wPop target\n",
    "    wedgeMaxR = [0.] * nWedges #will track the largest radius this wedge has seen (to cap yoyo bisection step)\n",
    "    wedgeAngle = [avgWedgeAngle] * nWedges #reset to equi-angle when starting each tract    \n",
    "    currentR = [0.]*nWedges\n",
    "    old_dr = [0.]*nWedges\n",
    "    wedgeStop = 0  #obsolete? this will flag if we need to rejigger oldR when we stopped an over-boundary wedge\n",
    "    for nW in range(nWedges):\n",
    "        currentR[nW] = tinyR  #each wedge starts as a tiny triangle of this radius\n",
    "        old_dr[nW] = currentR[nW] #this will track the last radial step (to help convergence)\n",
    "    oldR = [0.]*nWedges   #for remembering last loop's wedge radius\n",
    "    max_dr = [globalMax_dr]*nWedges  #reset max possible positive radial step size\n",
    "    for nW in range(nWedges) :  #this loop: set up tiny wedges in each direction to seed each wedge loop\n",
    "        angle[nW] = (nW-0.5)*wedgeAngle[nW]+angle0[t]   #local variable for orientation of START of wedge\n",
    "        angle2[nW] = angle[nW]+wedgeAngle[nW]      #local angle for clockwise END of wedge\n",
    "        pt1[nW] = tractCP\n",
    "        pt2[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle[nW]),  tractCP.y+currentR[nW]*math.sin(angle[nW]) )\n",
    "        pt3[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle2[nW]), tractCP.y+currentR[nW]*math.sin(angle2[nW]) )\n",
    "        wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ]) #build a tiny starter wedge triangle\n",
    "        wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)  #max prevents rare div-by-zero\n",
    "    HDpop = np.sum(wedgePop)\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1):\n",
    "        #go directly to jail, do not pass Go, this tract doesn't count\n",
    "        HDpop = avgDistrictPop  #white lie to kick us out of loop\n",
    "    while abs (HDpop - avgDistrictPop) > tolerPop :  #until we've grown the home district to the right size...\n",
    "        sumWedgePopGapChange = 0  #this will total wedge pops that fall short of expectations (when over boundary)\n",
    "        for nW in range(nWedges) :  #for each wedge, we'll build to gain pop or shrink to lose population ...\n",
    "            neededWedgePop = targetWedgePop[nW] - wedgePop[nW]\n",
    "            isSatisfied[nW] = 0  #default in case target changed last loop.  We'll check immediately below\n",
    "            if abs(neededWedgePop/targetWedgePop[nW]) < 0.5*toler :  #is this wedge close enough to stop iterating on it?\n",
    "                isSatisfied[nW] = 1\n",
    "            if isOverEdge[nW] == 0 and isSatisfied[nW] == 0:   #we skip over-boundary and near-perfect wedges\n",
    "                wedgePopDelta = wedgePop[nW] - oldWedgePop[nW]  #how much wedgePop gained in last loop\n",
    "                if (wedgePopDelta == 0): #our last wedge change was in an empty area (desert or off map)\n",
    "                    gainAdjust = EulerGain/gain  #Euler method often over-cautious at map edges or in sparse areas\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] * targetWedgePop[nW] / wedgePop[nW]  #Euler guess\n",
    "                    # can't use Newton-Raphson since last dy was zero, use Euler instead\n",
    "                    print(\"we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop\",t,tractLoopCounter[t],\n",
    "                          nW, round(180./pi*wedgeAngle[nW],1),round(currentR[nW],4),round(wedgePop[nW],1))\n",
    "                else: #use N-R estimation\n",
    "                    gainAdjust = 1.0\n",
    "                    R2delta = currentR[nW]*currentR[nW] - oldR[nW]*oldR[nW]  #this and below are run/rise of current slope\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] + R2delta / wedgePopDelta * neededWedgePop\n",
    "                    \n",
    "                guessedRsquared = max( guessedRsquared, 0. ) #minimum guardrail\n",
    "                guessed_dr = guessedRsquared ** 0.5 - currentR[nW]  #best guess for wedge radius change\n",
    "                #gain = max(0.3,normalGain ** (1. + yoyoFactor * yoyoCount[nW]))  #obsolete; using bisect\n",
    "                dr = gain * gainAdjust * guessed_dr   #gain typically ~0.8 for stability\n",
    "                dr = max( -0.5*currentR[nW],min(dr,max_dr[nW]) )  #apply min and max guardrails to wedge radius change\n",
    "                \n",
    "                if np.sign(dr) != np.sign(old_dr[nW]) :  #are we yoyoing? How many times?\n",
    "                    yoyoCount[nW] += 1\n",
    "                if yoyoCount[nW] > 3 and yoyoCount[nW] <= 6 :\n",
    "                    wedgeMaxR[nW] = max(wedgeMaxR[nW],currentR[nW]) #will track largest R in yoyo cycle\n",
    "                    max_dr[nW] = 2.*wedgeMaxR[nW]  #this will now store a reasonable max bisection step size\n",
    "                if yoyoCount[nW] > 6 :  #switch to bisection method; too many yo-yo's\n",
    "                    print(yoyoCount[nW],\"yoyos for tract,wedge,wedgePop,r=\",t,nW,wedgePop[nW],round(currentR[nW],4) )\n",
    "                    max_dr[nW] = 0.5 * max_dr[nW] #we are now bisecting ...\n",
    "                    if(neededWedgePop < 0):\n",
    "                        dr = max(-1.*max_dr[nW], -0.5*currentR[nW])  #reduce wedge size\n",
    "                    else:\n",
    "                        dr = max_dr[nW]  #increase wedge size\n",
    "                old_dr[nW] = dr  #these three lines -- save current as old values for next loop\n",
    "                oldR[nW] = currentR[nW]\n",
    "                oldWedgePop[nW] = wedgePop[nW]\n",
    "                if dr > 0. :  #we are growing a wedge trapezoid piece \n",
    "                    outerR = currentR[nW] + dr\n",
    "                    innerR = currentR[nW]\n",
    "                    currentR[nW] = outerR    #for next loop around\n",
    "                else:    #this wedge trapezoid piece will be SUBTRACTED from current wedge\n",
    "                    outerR = currentR[nW]\n",
    "                    innerR = currentR[nW] + dr  #remember, dr is negative here\n",
    "                    currentR[nW] = innerR             #for next loop around\n",
    "                #now, describe the new wedge to probe for precinct intersections ...\n",
    "                pt1[nW] = Point(tractCP.x+innerR*math.cos(angle[nW]), tractCP.y+innerR*math.sin(angle[nW]) )\n",
    "                pt2[nW] = Point(tractCP.x+outerR*math.cos(angle[nW]), tractCP.y+outerR*math.sin(angle[nW]) )\n",
    "                pt3[nW] = Point(tractCP.x+outerR*math.cos(angle2[nW]),tractCP.y+outerR*math.sin(angle2[nW]) )\n",
    "                pt4[nW] = Point(tractCP.x+innerR*math.cos(angle2[nW]),tractCP.y+innerR*math.sin(angle2[nW]) )\n",
    "                wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW],pt4[nW]])  #true for wedge add-on or to-be-trimmed\n",
    "                \n",
    "                printPoly[nW] = wedgePoly  #for debugging\n",
    "                latestWedgePop = 0.  #for the new piece, not the entire triangle\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                usedPrecinct = [0]*nPrecincts\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    if isEmptyGrid[nG] == 0: #NEW 3/18/22 TO SAVE A LITTLE TIME, skip empty grids\n",
    "                        gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                        if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                            for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                                nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                                if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                    usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                    overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                    if overlap > 0 :\n",
    "                                        fracArea = overlap/tractArea[nTT]\n",
    "                                        latestWedgePop  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                                        loopTractUse[nTT] += np.sign(dr)*overlap/tractArea[nTT] * tractPop[t]/avgDistrictPop\n",
    "                            # found all possible tract overlaps with this increment / decrement to this wedge.\n",
    "                        \n",
    "                wedgePop[nW] += np.sign(dr)*latestWedgePop  #for full triangle, based on this latest piece\n",
    "                # Now, flag if we're beyond boundary\n",
    "                if wedgePop[nW] < targetWedgePop[nW] : #if growing, confirm we're not beyond MAP boundary\n",
    "                    leadingEdge = LineString([pt2[nW],pt3[nW]])\n",
    "                    if leadingEdge.intersects(MAP) :  #still room to grow in part of edge, keep going\n",
    "                        gerrymandering = \"evil\"  #it had to be said\n",
    "                    else:  #this wedge is fully beyond the map. give up on more map intersection\n",
    "                        isOverEdge[nW] = 1\n",
    "                        shortedWedge = nW  #ID'ing highest numbered wedge that got shorted by the boundary in this loop\n",
    "                        oldWedgePopGap = wedgePopGap[nW]\n",
    "                        wedgePopGap[nW] = targetWedgePop[nW] - wedgePop[nW]  #how far this wedge's pop is below its target\n",
    "                        nActiveWedges -= 1  #a few lines below, we will adjust other wedge's targets\n",
    "                        sumWedgePopGapChange += wedgePopGap[nW] - oldWedgePopGap\n",
    "                        if (nActiveWedges == 0) : #we're doomed, somehow all wedges are short and off map\n",
    "                            print(\"PUNT! no more active wedges for tract, loop =\",t,tractLoopCounter[t] )\n",
    "                            tractLoopCounter[t] = nGiveUp + 1  #PUNT !!!\n",
    "                            \n",
    "                        max_dr = [globalMax_dr]*nWedges   #allow other wedges to take big steps again to catch up\n",
    "                        yoyoCount = [0] * nWedges  #go back to original gains on ALL active wedges\n",
    "                   \n",
    "        # end of nW loop to adjust all wedge populations by growing or trimming wedges, IDing over-edge wedges\n",
    "        tractLoopCounter[t] +=1   # still looping on home district Pop.\n",
    "        nReceivingWedges = nActiveWedges\n",
    "        oppFlag = 0\n",
    "        if 0 == 1: #always false; for HD2 we would have checked here for opp wedge restriction; ignore this block\n",
    "            # targetWedgePop[int(nWedges/2)] < 0.99 * avgDistrictPop/float(nWedges): #is opp wedge restricted?\n",
    "            oppFlag = 1\n",
    "            nReceivingWedges -= 1-isOverEdge[int(nWedges/2)]  #if yes, don't count opposite wedge as a receiver\n",
    "        if nReceivingWedges == 0: #rarely, the opposite wedge is the only active one\n",
    "            for nWW in range(nWedges): #in this case, we allow the opposite wedge to pick up the slack\n",
    "                targetWedgePop[nWW] += sumWedgePopGapChange/max(1.,float(nReceivingWedges) )\n",
    "        else: #if the opp wedge has a ceiling wedgePopTarget, distribute wedgePopGap to other active wedges\n",
    "            for nWW in range(nWedges):  #time to make adjustments in target wedge pops based on boundary fails\n",
    "                if nWW != int(nWedges/2) or oppFlag == 0 :  #excluding flagged opposite wedge as a receiver\n",
    "                    targetWedgePop[nWW] += sumWedgePopGapChange/float(nReceivingWedges)\n",
    "\n",
    "        # *** NEW 1/19/22 CODE TO ADJUST WEDGE ANGLES WHEN A BOUNDARY ENCOUNTERED\n",
    "        if (nActiveWedges == nWedges or nActiveWedges < nWedges - 2\n",
    "           or didWeRestart[t] == 1) :  #0 or 3+ over-boundary short wedges, or we've already adjusted wedge angles once\n",
    "            HDpop = np.sum(wedgePop) #Keep going with normal wedge growth and trim process\n",
    "        else :  # 1 OR 2 SHORTED WEDGES.  MAY WANT TO ALTER WEDGE ANGLES\n",
    "            didWeRestart[t] = 1  #to ensure we adjust wedge angles and target wedgePops at most once per tract\n",
    "            if (nActiveWedges == nWedges - 2) :  #exactly two wedges got shorted in this loop -- are they opposing?\n",
    "                oppW = int( (shortedWedge + nWedges/2) % nWedges)  #the index of wedge opposite the last shorted wedge\n",
    "                if (isOverEdge[oppW] == 0) :\n",
    "                    print(\"we have 2 non-opposing shorted wedges for tract no\",t)\n",
    "                    totalLiveAngle = 2.*pi - np.sum(isOverEdge)*avgWedgeAngle  #avail angle to divvy among live wedges  \n",
    "                    for nW in range (nWedges) : #Decrease wedge angles opposite shorted wedges via a complex weighting\n",
    "                        if isOverEdge[nW] == 0 :\n",
    "                            oppW = int( (nW + nWedges/2) % nWedges) \n",
    "                            angleWeight = (np.sum(wedgePopGap)-wedgePopGap[oppW])/np.sum(wedgePopGap)/(nActiveWedges-1)\n",
    "                            wedgeAngle[nW] = angleWeight * totalLiveAngle  \n",
    "                else : #shorted wedges ARE opposing\n",
    "                    print(\"we have 2 OPPOSING shorted wedges for tract no\",t) # no wedge-angle adjustment in this case\n",
    "                    #wedgeAngle=[avgWedgeAngle]*nWedges  #comment this out, no change\n",
    "            else: # a single shorted wedge (over boundary).  COMPLETELY RESTART wedge growth process\n",
    "                shortW = shortedWedge #Next dozen lines: convoluted code to find angle to closest boundary point\n",
    "                #print(\"1 wedge over map boundary for tract\",t,\"at loop, wedgePop\",tractLoopCounter[t],wedgePop[shortW] )\n",
    "                shortedPoly = Polygon([tractCP, pt2[shortW],pt3[shortW] ])\n",
    "                MAPedge = shortedPoly.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "                minDistance = max(tractCP.distance(MAPedge),tinyR)  #closest distance to edge where wedge hit the boundary\n",
    "                edgeCircle = tractCP.buffer(1.01*minDistance)  #build a circle a little bigger than this distance\n",
    "                #  1.001 is not big enough; will occasionally not intersect\n",
    "                closeArea = edgeCircle.intersection(MAPedge)\n",
    "                counter = 0.\n",
    "                maxCounter = 10. #give up after 10 tries\n",
    "                while closeArea.is_empty :  # protect against missing the map boundary somehow\n",
    "                    print(\"need to widen edge circle beyond\",minDistance, \"for tract (x,y)=(\",tractCP.x,tractCP.y)\n",
    "                    minDistance = minDistance * 1.1\n",
    "                    edgeCircle = tractCP.buffer(minDistance)  #widen the circle\n",
    "                    closeArea = edgeCircle.intersection(MAPedge)\n",
    "                    counter += 1\n",
    "                    if counter >= maxCounter: #not finding map edge intersection.  Try another way\n",
    "                        print(\"did not find boundary with circle approach.  Brute-force it.\")\n",
    "                        minDistance = tractCP.distance(MAP.exterior)\n",
    "                        edgeCircle = tractCP.buffer(1.01*minDistance)\n",
    "                        closeArea = edgeCircle.intersection(MAP.exterior)                        \n",
    "            \n",
    "                closePoint = closeArea.centroid  # this is a point very close to the closest beeline from tract to map edge\n",
    "                #print(closePoint,tractCP, \"are boundary point and tract center point\")\n",
    "                x1 = closePoint.x\n",
    "                x2 = tractCP.x  #debugging\n",
    "                dx = closePoint.x - tractCP.x\n",
    "                dy = closePoint.y - tractCP.y\n",
    "                exitAngle = pi/2. * np.sign(dy)  #default in case dx=0\n",
    "                if (dx != 0 ):\n",
    "                    exitAngle = math.atan(dy/dx)  #this is the angle from the x-axis to the exit beeline\n",
    "                    if dx < 0 :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                        exitAngle = pi + exitAngle\n",
    "                angle0[t] = exitAngle # this reorients 0th wedge to face boundary's closest point               \n",
    "                # New 1/23/22 - let's estimate wedgePopGap at ideal orientation, normal angle   # ****************\n",
    "                wedgeAngle1 = exitAngle - 0.5*avgWedgeAngle\n",
    "                wedgeAngle2 = exitAngle + 0.5*avgWedgeAngle\n",
    "                maxR = max(stateWidth,stateHeight) #ensuring we make a big enough wedge\n",
    "                wedgePt1 = tractCP\n",
    "                wedgePt2 = Point(tractCP.x+maxR*math.cos(wedgeAngle1),  tractCP.y+maxR*math.sin(wedgeAngle1) )\n",
    "                wedgePt3 = Point(tractCP.x+maxR*math.cos(wedgeAngle2), tractCP.y+maxR*math.sin(wedgeAngle2) )\n",
    "                wedgePoly = Polygon([ wedgePt1, wedgePt2, wedgePt3 ])\n",
    "                minWedgePop[t] = 0. #the wedgePop of the shorted wedge if we re-orient but don't adjust angles\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    minWedgePop[t]  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                #print(\"the minimum shorted Wedge Pop for tract {0} is {1}\".format(t,minWedgePop[t]) )  #debug\n",
    "                #printAngle[t] = round(exitAngle*180./pi,4) #for debugging\n",
    "                printDist = round(minDistance,4)\n",
    "                #print(t,\"(\",tractCP.x,tractCP.y,\")\",printAngle,printDist,\"t,tract(x,y),exitAngle,dist\")\n",
    "                # ********************************************************************\n",
    "                # assign new wedge angles.  The two wide angles face toward and away from nearest boundary\n",
    "                #ratio = wedgePopGap[shortW]/ (avgDistrictPop/nWedges)  #0-1; this is degree of shortness\n",
    "                ratio = 1. - minWedgePop[t]/ (avgDistrictPop/nWedges)  # *** UPDATED\n",
    "                # printWedgePopGap[t] = wedgePopGap[shortW]\n",
    "                wideAngle = (1. + coeff1 * ratio + coeff2 * ratio*ratio)*avgWedgeAngle #HERE, WE ADJUST ANGLES\n",
    "                wideAngle = min(wideAngle, maxWideAngle*pi) #set an upper limit\n",
    "                thinAngle = (2.*pi - 2.*wideAngle)/(nWedges - 2.)  #other wedges equally angle-compressed\n",
    "                #if (t % 9 == 0):  #occasional print\n",
    "                    #print(\"tract,wide, thin angles are \",t,round(180/pi*wideAngle,4),round(180/pi*thinAngle,4) )\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0 or nW == int(nWedges/2) ) : #assign these two as shorted wedge and its opposite\n",
    "                        wedgeAngle[nW] = wideAngle\n",
    "                    else :\n",
    "                        wedgeAngle[nW] = thinAngle\n",
    "                #** COMPLETELY RESTART WEDGE GROWTH PROCESS FROM INITIAL PIZZA SLICES, NOW RE-ORIENTED\n",
    "                loopTractUse = [0.] * nTracts  # reset as we are starting over\n",
    "                loopPrecinctUse = [0.]* nPrecincts\n",
    "                HDpop = 0.\n",
    "                nActiveWedges = nWedges\n",
    "                targetWedgePop = [avgDistrictPop/nWedges] * nWedges\n",
    "                oldR = [0.]*nWedges   #for remembering last loop's wedge radius  \n",
    "                currentR = [tinyR]*nWedges                \n",
    "                old_dr = [tinyR]*nWedges  #this will track the last radial step (to help convergence)\n",
    "                max_dr = [globalMax_dr]*nWedges  #reset max step size\n",
    "                wedgePop = [0.]*nWedges\n",
    "                oldWedgePop = [0.]*nWedges #wedge pop from previous round\n",
    "                wedgePopGap = [0.]*nWedges\n",
    "                isOverEdge = [0] *nWedges\n",
    "                isSatisfied = [0] *nWedges\n",
    "                yoyoCount = [0] *nWedges #just in case we missed this earlier\n",
    "                wedgeMaxR = [0.] * nWedges #resetting\n",
    "                angle[0] = angle0[t] - 0.5*wideAngle  #the 0th wedge always faces the boundary\n",
    "                angle2[0] = angle[0] + wedgeAngle[0]\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0):\n",
    "                        angle[nW] = angle0[t] - 0.5*wideAngle  #we have to start somewhere :-)\n",
    "                    else :\n",
    "                        angle[nW] = angle2[nW-1]\n",
    "                    angle2[nW] = angle[nW]+wedgeAngle[nW]      \n",
    "                    pt1[nW] = tractCP\n",
    "                    pt2[nW] = Point(tractCP.x+tinyR*math.cos(angle[nW]),  tractCP.y+tinyR*math.sin(angle[nW]) )\n",
    "                    pt3[nW] = Point(tractCP.x+tinyR*math.cos(angle2[nW]), tractCP.y+tinyR*math.sin(angle2[nW]) )\n",
    "                    wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ])\n",
    "                    wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)\n",
    "                HDpop = np.sum(wedgePop)   #** END OF RESTART BLOCK.  RE-ENTER MAIN LOOP FOR THIS TRACT\n",
    "                      \n",
    "        # **** BELOW IS TO CORRECT FOR NONCONVERGENCE *****\n",
    "        if (tractLoopCounter[t] > nLoopPrint):  #may be becoming unstable. Alert user,    #and (don't) reduce gain\n",
    "            strPop = str(round(HDpop,2))\n",
    "            strWdg = \"Overedge?\"\n",
    "            strSat = \"Satisfied?\"\n",
    "            strYoyo = \"yoyo?\"                \n",
    "            strTWP_dr = \"tWP,dr\"\n",
    "            for nWW in range(nWedges) :\n",
    "                strPop = strPop + \", \"+str(round(wedgePop[nWW],1) )\n",
    "                strWdg = strWdg +str(isOverEdge[nWW])+\", \"\n",
    "                strSat = strSat +str(isSatisfied[nWW])\n",
    "                strYoyo = strYoyo + str(yoyoCount[nWW])\n",
    "                strTWP_dr = strTWP_dr + \", \"+str(round(targetWedgePop[nWW],1) )+\",\"+str(round(old_dr[nWW],4) )\n",
    "            print(\"loop{0}, tr{1},wedgePops{2}, {3},{4},{5} \".format(tractLoopCounter[t],t,strPop,strWdg,strSat,strYoyo) )\n",
    "            print(\"   targetWP, latest drx4 are\",strTWP_dr)\n",
    "\n",
    "        if (tractLoopCounter[t] > nLoopSuperPrint) :\n",
    "            for nWW in range(nWedges):                \n",
    "                print(\"wedge no, currentR,oldWedgePop, wedgePop, overEdge?\",nWW,str(round(currentR[nWW],5)), \n",
    "                      str(round(oldWedgePop[nWW],4)), str(round(wedgePop[nWW],4)),isOverEdge[nWW])   #debug\n",
    "                x, y = printPoly[nWW].exterior.xy     #wedge debugging .....\n",
    "                plt.plot(x, y, c=(0.1, 0.2, 0.02+float(nWW)/nWedges) )\n",
    "            plt.show()\n",
    "        if(tractLoopCounter[t] >= nGiveUp):\n",
    "            print(\"I looped {0} times on tract {1}, giving up w pop {2}\".format(nGiveUp,t,HDpop) )\n",
    "            wrongPop[t] = HDpop  #this will flag this HD as triaged\n",
    "            HDpop = avgDistrictPop  #white lie to kick out of loop\n",
    "        # *** END OF TRIAGE FOR NONCONVERGENCE\n",
    "        \n",
    "    # END OF WHILE LOOP --> we are within tolerance of avgDistrictPop. Finalize this tract's Home District stats\n",
    "    for nTT in range (nTracts) :\n",
    "        tractUse[nTT] += loopTractUse[nTT]\n",
    "    totGOP = 0.\n",
    "    totVote = 0.\n",
    "    totVAP = 0.\n",
    "    totHisp = 0.\n",
    "    totBlack = 0.  #zero these out prior to summing over final wedges\n",
    "    usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "    usedPrecinct = [0]*nPrecincts\n",
    "\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1): \n",
    "        #we bypassed the big loop; this tract is inconsequential\n",
    "        HDvPop[t] = HDpop  #a white lie\n",
    "        HDweight[t] = 0.000001  #to suppress this in total stats\n",
    "    else :\n",
    "        HDvPop[t] = np.sum(wedgePop)\n",
    "        # HDvHisp[t] = np.sum(wedgeHisp)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        # HDvBlack[t] = np.sum(wedgeBlack)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        HDweight[t] = tractPop[t]/np.sum(tractPop)\n",
    "        nearEdge[t] = nWedges - nActiveWedges  #flagging the number of wedges that were not completely pop-filled\n",
    "        centerPt = tractCP\n",
    "        outerPt2 = Point(tractCP.x+currentR[0]*math.cos(angle[0]), tractCP.y+currentR[0]*math.sin(angle[0]) )\n",
    "        outerPt3 = Point(tractCP.x+currentR[0]*math.cos(angle2[0]),tractCP.y+currentR[0]*math.sin(angle2[0]) )\n",
    "        HDpolly = Polygon([centerPt,outerPt2,outerPt3])   #initiate a polygon that will be the home district\n",
    "        for nW in range(nWedges):\n",
    "            HDradius[t][nW] = currentR[nW]\n",
    "            HDangle[t][nW] = angle[nW]\n",
    "            \n",
    "        for nW in range(1,nWedges):  #save final geometry, compute minority and partisan stats\n",
    "            cR = currentR[nW] #shorthand\n",
    "            outerPt2 = Point(tractCP.x+cR*math.cos(angle[nW]), tractCP.y+cR*math.sin(angle[nW]) )\n",
    "            outerPt3 = Point(tractCP.x+cR*math.cos(angle2[nW]),tractCP.y+cR*math.sin(angle2[nW]) )\n",
    "            HDpolly =  HDpolly.union( Polygon([centerPt,outerPt2,outerPt3]) )  #add this wedge to HD district polygon\n",
    "        HDpolly = HDpolly.intersection(MAP)  #exclude map's convex hull and buffer, just the original union of precincts\n",
    "        HDarea[t] = HDpolly.area  #for stats, final Home District area\n",
    "        for nG in range(nGrids) :  # ID the ACTIVE grids to look for intersecting precincts\n",
    "            gridIntersxn = gridGeom[nG].intersection(HDpolly)\n",
    "            if (gridIntersxn.area > 0) :  #this grid is RELEVANT to the final Home District\n",
    "                for tt in range(nGridTracts[nG]):\n",
    "                    nTT = gridTractNo[nG][tt]\n",
    "                    if usedTract[nTT] == 0: #only examine tracts that have not already been called for intersection\n",
    "                        usedTract[nTT] == 1  #this tract has now been called \n",
    "                        overlap = tractGeom[nTT].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/tractArea[nTT]\n",
    "                            totVAP += fracArea*tractVAP[nTT]\n",
    "                            totHisp += fracArea*tractHisp[nTT]\n",
    "                            totBlack += fracArea*tractBlack[nTT]\n",
    "                        \n",
    "                for pp in range(nGridPrecincts[nG]): #scanning the list of precincts in this active grid\n",
    "                    nPP = gridPrecinctNo[nG][pp]\n",
    "                    if usedPrecinct[nPP] == 0  :\n",
    "                        usedPrecinct[nPP] = 1  #don't double up on precinct intersection\n",
    "                        overlap = vtdGeom[nPP].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/vtdArea[nPP]\n",
    "                            totGOP += fracArea*vtdGOP[nPP]*vtdPop[nPP]\n",
    "                            totVote += fracArea*vtdPop[nPP]\n",
    "                            loopPrecinctUse[nPP] += overlap/vtdArea[nPP] *tractPop[t]/avgDistrictPop\n",
    "        HDvHisp[t] = totHisp/totVAP\n",
    "        HDvBlack[t] = totBlack/totVAP\n",
    "        HDvGOP[t] = totGOP / totVote\n",
    "        for nPP in range (nPrecincts) :\n",
    "            precinctUse[nPP] += loopPrecinctUse[nPP] #add to global use for this precinct\n",
    "            \n",
    "        \n",
    "\n",
    "    # end of loop on this tract\n",
    "for t in range(nTracts):\n",
    "    if(wrongPop[t] > 0):\n",
    "        HDvPop[t] = wrongPop[t]  #undo the lie that got us out of the loop early\n",
    "HDsumWeight = np.sum(HDweight)\n",
    "print(\"Sum of weights over all precinct home districts should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea) )\n",
    "stateGOP2 = 0.\n",
    "stateHisp2 = 0.\n",
    "stateBlack2 = 0.\n",
    "for t in range(nTracts):\n",
    "    HDweight[t] = HDweight[t]/HDsumWeight   #renormalizing\n",
    "    stateGOP2 += HDweight[t]*HDvGOP[t]\n",
    "    stateBlack2 += HDweight[t]*HDvBlack[t]\n",
    "    stateHisp2 += HDweight[t]*HDvHisp[t]\n",
    "statePop = np.sum(tractPop)\n",
    "print(\"calculated statewide vote was {0}, should have been {1}\".format(stateGOP2, stateGOP) )\n",
    "print(\"calcd statewide Hispanic pop was {0}, should have been {1}\".format(stateHisp2, np.sum(tractHisp)/stateVAP ) )\n",
    "print(\"calcd statewide Black pop was {0}, should have been {1}\".format(stateBlack2, np.sum(tractBlack)/stateVAP ) )\n",
    "print(\"fraction of HDs that with altered wedge angles near boundaries = \",np.sum(didWeRestart)/nTracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "24a347a9-8220-4c1a-bcfc-14923f1f98f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of weights over all precinct home districs should have been 1.000, but was  1.0003391221945768\n",
      "Average and max number of wedgePop loops per tract were:  7.287209302325581 40.0\n",
      "min,average,max of Home District areas were:  0.0 0.6129764281638655 4.027851352404535 for  FL\n"
     ]
    }
   ],
   "source": [
    "#use this line to document progress\n",
    "# start FL 12:04, end 2:23\n",
    "print(\"Sum of weights over all precinct home districs should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea),\"for \",STATE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "72247942-899a-4ab2-9796-239aa1e524fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FL 18Mar\n"
     ]
    }
   ],
   "source": [
    "date = \"18Mar\"\n",
    "print(STATE, date)  #check that I will not overwrite existing file :-)\n",
    "tractCPx = [0.]*nTracts\n",
    "tractCPy = [0.]*nTracts\n",
    "tractNo = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractCPx[t]=tractGeom[t].centroid.x\n",
    "    tractCPy[t]=tractGeom[t].centroid.y\n",
    "    tractNo[t]=t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "c55ac5fd-7402-47b9-9093-10b9fe0912b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LET'S WRITE AN OUTPUT FILE BEFORE WE FORGET :-)\n",
    "#convert HD wedge geometries to 1D arrays.  BELOW IS FOR 4-WEDGE.  CAN AUGMENT FOR 6-WEDGE\n",
    "HDangle0 = [0.]*nTracts\n",
    "HDradius0 = [0.]*nTracts\n",
    "HDangle1 = [0.]*nTracts\n",
    "HDradius1 = [0.]*nTracts\n",
    "HDangle2 = [0.]*nTracts\n",
    "HDradius2 = [0.]*nTracts\n",
    "HDangle3 = [0.]*nTracts\n",
    "HDradius3 = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    HDangle0[t] = HDangle[t][0]\n",
    "    HDradius0[t] = HDradius[t][0]\n",
    "    HDangle1[t] = HDangle[t][1]\n",
    "    HDradius1[t] = HDradius[t][1]\n",
    "    HDangle2[t] = HDangle[t][2]\n",
    "    HDradius2[t] = HDradius[t][2]\n",
    "    HDangle3[t] = HDangle[t][3]\n",
    "    HDradius3[t] = HDradius[t][3]\n",
    "# now convert output to pandas dataframe and export\n",
    "\n",
    "paramList = [\"STATE\",\"stateGOP\",\"nDistricts\",\"nTracts\",\"nPrecincts\",\"nWedges\",\"popn-toler\",\n",
    "             \"gain\",\"coeff1\",\"coeff2\"]\n",
    "paramValues = [STATE,stateGOP, nDistricts, nTracts,nPrecincts,nWedges, toler, gain, coeff1,coeff2]\n",
    "for i in range(nTracts-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "df = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"HD-pop\":HDvPop,\"HDvGOP\":HDvGOP,\"HDvHisp\":HDvHisp,\n",
    "                    \"HDvBlack\":HDvBlack,\"HDwt\":HDweight,\"HDarea\":HDarea, \"HDangle0\":HDangle0, \"HDangle1\":HDangle1,\n",
    "                    \"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\n",
    "                    \"HDradius2\":HDradius2,\"HDradius3\":HDradius3,\"startAngle\":angle0,\"tractNo\":tractNo,\n",
    "                    \"Loops\":tractLoopCounter,\"tractPop\":tractPop,\"tractHisp\":tractHisp,\"tractBlack\":tractBlack,\n",
    "                    \"centroid x\":tractCPx,\"centroid y\":tractCPy, \"tractUse\":tractUse,\"nearEdge\":nearEdge,\n",
    "                    \"wrongPop\":wrongPop,\"Restart?\":didWeRestart} ) \n",
    "\n",
    "outname = STATE+str(nDistricts)+\"HD1tol\"+str(toler)+\"nW\"+str(nWedges)+date+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)\n",
    "precinctNo = [0.]*nPrecincts\n",
    "vtdX = [0.]*nPrecincts\n",
    "vtdY = [0.]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    precinctNo[p]=p\n",
    "    vtdX[p] = vtdGeom[p].centroid.x\n",
    "    vtdY[p] = vtdGeom[p].centroid.y\n",
    "df2 = pd.DataFrame( {\"precinctNo\":precinctNo,\"precinctPop\":vtdPop,\"precUse\":precinctUse, \"vtdX\":vtdX, \"vtdY\":vtdY} )\n",
    "outname2 = STATE+date+\"_VTD_tol\"+str(toler)+\"nW\"+str(nWedges)+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname2\n",
    "df2.to_csv(outpath)  #currently, I'm outputting the precinct use stats to a separate file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "c4449da0-47a9-40cc-a585-8288447290e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1107 0.89 ( -82.49312791520413 27.359169327177035 ) 0.5513559880216108 t, pop/target,(x,y), pctR\n",
      "1370 1.01 ( -80.12708988527885 25.9654418899964 ) 0.36618368435885595 t, pop/target,(x,y), pctR\n",
      "4081 0.99 ( -82.68970197632349 27.786372447618056 ) 0.4864349293209161 t, pop/target,(x,y), pctR\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will redo tract numbers  [1107]\n"
     ]
    }
   ],
   "source": [
    "# Looking for nonconvergers ...  Run even if none observed, just to check  \n",
    "redo = [0]\n",
    "counter = 0.\n",
    "for t in range (nTracts) :\n",
    "    if wrongPop[t] > 0 :\n",
    "        ratio = round(wrongPop[t]/avgDistrictPop,2)\n",
    "        tractX = tractGeom[t].centroid.x\n",
    "        tractY = tractGeom[t].centroid.y        \n",
    "        print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        if (ratio < 0.9 or ratio > 1.1) :  #these we will redo\n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=14)\n",
    "            if counter == 0 :  #first redo\n",
    "                redo = [t]\n",
    "            else :\n",
    "                redo.append(t)\n",
    "            counter+= 1\n",
    "        else: #show these milder offenders on the map in a smaller font            \n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=9)\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()\n",
    "print(\"we will redo tract numbers \",redo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "be6e965b-4ca8-4af2-8031-63132fbb939d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "67 0.7415358745833479 2610 pop,GOP 0.8758620689655172\n",
      "68 0.6855023879081349 3296 pop,GOP 0.8492111650485437\n",
      "69 0.541256827636948 475 pop,GOP 0.8421052631578947\n",
      "70 0.497286106350765 2855 pop,GOP 0.7590192644483362\n",
      "71 0.4913376005140092 2882 pop,GOP 0.7238029146426093\n",
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting underused PRECINCTS\n",
    "for p in range (nPrecincts) :\n",
    "    if precinctUse[p] < 0.8 and isSkippedPrecinct[p] == 0 and vtdPop[p] > 0: #ignore suppressed precincts\n",
    "        print(p,precinctUse[p], vtdPop[p],\"pop,GOP\",vtdGOP[p])\n",
    "        pU = round(precinctUse[p],3)\n",
    "        tractX = vtdGeom[p].centroid.x\n",
    "        tractY = vtdGeom[p].centroid.y        \n",
    "        # print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,pU, fontsize=9)\n",
    "        if notPolyVTD[p] == 0 :\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "        else :\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "37f47a77-d58e-4b4c-be60-676819128e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (OPTIONAL) IF WE STOPPED AND RESTARTED, read the data back in:  #COULD UPDATE THIS W/ANGLES, RADII\n",
    "import pandas as pd\n",
    "infilename = \"FL27plus1HD1tol0.005nW418Mar.csv\"\n",
    "df2 = pd.read_csv(\"state_HD_output/\"+infilename)\n",
    "HDvPop = df2[\"HD-pop\"]\n",
    "HDvHisp = df2[\"HDvHisp\"]\n",
    "HDvGOP = df2['HDvGOP']\n",
    "HDweight = df2['HDwt']\n",
    "HDarea = df2['HDarea']\n",
    "tractLoopCounter = df2['Loops']\n",
    "tractPop = df2['tractPop']\n",
    "tractCPx = df2['centroid x']\n",
    "tractCPy = df2['centroid y']\n",
    "tractUse = df2['tractUse']\n",
    "# nearEdge = df2['nearEdge']   #add this back in, PLEASE\n",
    "df2.head()\n",
    "nTracts = len(tractPop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "69499dab-67d8-4434-99aa-f7061b8a5128",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FL , true number of districts = 28\n"
     ]
    }
   ],
   "source": [
    "nDistricts = nDistricts +1  #adding panhandle back in as a counted district\n",
    "print(STATE,\", true number of districts =\",nDistricts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e9b2c0c0-fb53-44e5-9155-1963d4833ee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "750596.7418101031 0.1131774380110593 0.0001361943772 0.0400616118781349 0.7450137754374908\n"
     ]
    }
   ],
   "source": [
    "#  HERE'S A BLOCK TO OVERWRITE THE REDONE DATA\n",
    "for r in range(nRedos):\n",
    "    t = redo[r]\n",
    "    HDvPop[t] = redoHDvPop[r]\n",
    "    HDvGOP[t] = redoHDvGOP[r]\n",
    "    tractArea[t] = redoHDarea[r]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c80e0880-6811-4408-b847-6d70bd2546ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt   #SKIP THIS BLOCK IF STARTING FROM FIRST BLOCK\n",
    "from scipy.stats import norm\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "58719e38-0d10-455c-be9b-b67082cb5e8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#run this block only if whole map was a multipolygon\n",
    "counter = 0\n",
    "for geom in wholeMAP.geoms:\n",
    "    if counter==0:\n",
    "        fullMAP = geom\n",
    "    counter+=1\n",
    "x,y = fullMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "plt.show()\n",
    "wholeMAP = fullMAP  #resetting full map to exclude the Keys\n",
    "fullMAP = \"toe fungus\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "c8f73bb9-e36b-41c7-9220-1ec09169aa3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all done with redo's.  Let's look at some outputs.  First, red/blue by home district  SHOULD BE MIX OF RED AND BLUE\n",
    "nPlot = 5\n",
    "for t in range(nTracts):\n",
    "    if(t % nPlot == 0 and tractPop[t] > minTractPop):\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,color='green')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "a5d3b510-6d1e-49ae-9a5d-03352e2b449e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map of tracts that were used less than 0.7 of expectation in FL\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now, where are those UNDERPERFORMING tracts (<0.x usage)\n",
    "maxPlot = 0.70\n",
    "print(\"map of tracts that were used less than {0} of expectation in {1}\".format(maxPlot, STATE) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] < maxPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy   #turn these on if I pull map back in\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "876011e8-d37e-44a6-8dd9-a1e4d2f51e58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of tracts that were used more than 1.2 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the OVERUSED tracts?\n",
    "minPlot = 1.2\n",
    "print(\"here is a map of tracts that were used more than {0} of expectation\".format(minPlot) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "19bfa7c8-0449-4182-8ed9-f5a1c1276f03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of 51 tracts that were used more than 1.6 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the REALLY OVERUSED tracts?\n",
    "minPlot = 1.6\n",
    "counter = 0\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "        counter +=1\n",
    "\n",
    "print(\"here is a map of {0} tracts that were used more than {1} of expectation\".format(counter, minPlot) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "f3e9bde3-06f6-402d-bce1-62b5631e8db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Sfi1pdZnpCyVtlbQyfV0+ytjNzGwMZGkj6AUOA+4maRs4LSLWZ6j7a8DVJEcT5fw0Is7KUJeZmeUkSxvBoojYNNqKI+JOSZ2jD8nMzGopy6mhUSeBUVgg6QFJyyTNLldI0oWSeiX1btqUZzhmZo0nU6dzOVkBzIqI1wCfA24pVzAiro2I7ojo7ujoqFV8ZmYNYdwSQURsG37QTfpM5FZJU8crHjOzRlW2jUDS2ZVmjIibDmTBkqYBT0ZESJpHkpT8LGQzsxqr1Fj81vTvi4BTSS4dBfgtYDlQMRFIWgIsBKZK2gB8DGgFiIjFwDnAByQNAjuB8yIi9mstzMxsv5VNBBFxAYCkW4FXRsTGdHg68PlqFUdExQfcR8TVJJeXmpnZOMrSRtA5nARSTwIvzykeMzOrsSz3ESyX9ENgCRDAecAduUZlZmY1UzURRMTFkt7GCw+svzYibs43LDMzq5UsRwSQXPO/PSJ+JGmCpEkRsT3PwMzMrDaqthFI+hPgP4Br0lHHUuHmLzMzO7RkaSz+IMmDabYBRMQvSC4pNTOzOpAlEeyKiN3DA5JaSBqNzcysDmRJBD+RdBlwhKS3AN8Gvp9vWGZmVitZEsGlwCaSh9f/KbAU+Ns8gzIzs9rJcvloAfhS+jIzszpTqdO5VVRoC4iIV+cSkZmZ1VSlIwI/QtLMrAFU6nQuy3OJzczsEFfp1NB2Sp8aEhARcVRuUZmZWc1UOiKYVMtAzMxsfFQ6IjgqIrZJOqbU9Ih4Or+wzMysVio1Ft9I0mDcR3KKSEXTAujKMS4zM6uRSongyvTviRHxfC2CMTOz2qt0Z/Fn07931yIQMzMbH5WOCAYkfRWYIemqkRMj4pL8wjIzs1qpdkPZm4E3kbQTmJlZHap0+ehTwDclPRwRD9QwJjMzq6Esj6p8PO2GurO4fES8L6+gzMysdrIkgu8CPwV+BAzlG46ZmdValkQwISL+erQVS7qOpJ3h1xExp8R0kVyZdAawA3hvRKwY7XLMzOzAZEkEt0o6IyKWjrLurwFXA18vM30RcEL6OgX4Yvo3F52X/mDP+19eeWZeizlk9a3fQs+6zUyZ0MaWHbuZ39UOQM+6zczvauekWVMy1zGyfN/6LXxnxQbWPrmdXYMFFnS1M+mIVn7x5HZWPvYMRx/RyjM7Bzh99jQuPeNEAG6891GWrd7I7OlH8T9PPcfKR7ewa7DAhNZmNu/YzVAhaBYMFPLZHuNNQJOg48jDaGkWAUw+opWtzw9CBNt2DvDc7iFam0RTk+hsn8j5Czrpf3wrv3hyO08/t5tjJrZx9IQ2AKZOOow5L5m857M9adYU+tZv4VPLHmbtpud4acdELl2UbPtyn3mpz7fcZz4afeu3cNOKDQTw9rkzKi63OL6RsZYq94snt9OzbjMzj5nAH7xuxl7rby9QROXHD6edz00EdgEDjKLTOUmdwK1ljgiuAZZHxJJ0eA2wMCI2Vqqzu7s7ent7qy16L8VJYJiTwQv61m/hXV/uYfdggUIkH3Brs0BicKhAW0sTN7x/fsV/nuI6isv3rd/CO669h91D2R5zfdFpXcxsn8hlN68ao7WzYk2CtpYmLj9rNn/33VUMFSXSJqClpankZ17q8wVKfuaj0bd+C+/4UlIHQFuzWHLhgpLLbWlugggGC0FL09775+VnzeaKW/uTcum04TpLrf/+xHqok9QXEd2lplV9VGVETIqIpog4IiKOSofHoufRY4HHioY3pOP2IelCSb2Sejdt2jQGi7ZiPes270kCkPQfMjAUDKTjBgYL9KzbnLmO4vI96zYzkDEJANzW/wTLVlf8LWAHYPjzWbZ6415JAKAAZT/zUp9vuc98NHrWbWag6At7YCgqLndgKJL3I/bPZas3vlAunVZp/fcn1npWNRFIOq3UawyWrRLjSn5jRMS1EdEdEd0dHR1jsGgrNr+rnbaWJprST6SJ5IigtaWJZkFrS9Oew+1qdYwsP7+rPTm6yOj02dNYNGf6/q6KVdGUfj6L5kynecR/fxOU/cxLfb7lPvPRmN/VTmvLC4G0NqviclublbwfsX8umjP9hXLptErrvz+x1rMsp4a+XzR4ODAP6IuIN1Wt/CA5NQRuI6jGbQQHF7cRlF5ucXwjY3UbQWWVTg1VTQQlKjsO+KeIeEeGsp2UTwRnAheTXDV0CnBVRMyrVuf+JgIzs0ZWKRFkuWpopA3APl/sJRa6BFgITJW0AfgY0AoQEYuBpSRJYC3J5aMX7EcsZmZ2gKomAkmf44Vz903Aa4GqXU5UO2KI5FDkg9VDNDOzPGU5Iig+DzMILImIu3KKx8zMaqxqIoiI62sRiJmZjY+ql4+amVl9cyIwM2twZROBpH9P/36oduGYmVmtVToiOEnSLOB9kqZIOqb4VasAzcwsX5UaixcDtwFdJI+qLO4nINLxZmZ2iCt7RBARV0XEicB1EdEVEccXvZwEzMzqRJbLRz8g6TXAb6aj7oyIB/MNy8zMaiVL76OXADcAL0pfN0j687wDMzOz2shyZ/H7gVMi4jkASZ8C7gE+l2dgZmZWG1nuIxB7P7R+iNLPEjAzs0NQliOCrwL3Sro5Hf4D4Cu5RWRmZjWVpbH4XyQtB95AciRwQUT8PO/AzMysNjI9jyAiVgArco7FzMzGgfsaMjNrcE4EZmYNrmIikNQs6Ue1CsbMzGqvYiKIiCFgh6TJNYrHzMxqLEtj8fPAKkm3A88Nj4yIS3KLyszMaiZLIvhB+jIzszqU6ZnFko4AZkbEmhrEZGZmNZSl07m3AitJnk2ApNdK+l7OcZmZWY1kuXz048A84BmAiFgJHJ9bRGZmVlNZEsFgRGwdMS6yVC7pdElrJK2VdGmJ6QslbZW0Mn1dnqVeMzMbO1kai1dLeifQLOkE4BLg7mozSWoGPg+8BdgA3C/pexHx0IiiP42Is0YZt5mZjZEsRwR/DswGdgFLgG3AhzPMNw9YGxHrImI38E3g9/czTjMzy0mWq4Z2AH+TPpAmImJ7xrqPBR4rGt4AnFKi3AJJDwCPAx+JiP6RBSRdCFwIMHPmzIyLNzOzLLJcNXSypFXAgyQ3lj0g6aQMdZd6eM3ItoUVwKyIeA3JE89uKVVRRFwbEd0R0d3R0ZFh0WZmllWWU0NfAf4sIjojohP4IMnDaqrZABxXNDyD5Ff/HhGxLSKeTd8vBVolTc0SuJmZjY0siWB7RPx0eCAifgZkOT10P3CCpOMltQHnAXvdfyBpmiSl7+el8WzOGryZmR24sm0Ekuamb++TdA1JQ3EA5wLLq1UcEYOSLgZ+CDQD10VEv6SL0umLgXOAD0gaBHYC50VEpktTzcxsbKjc966kOyrMFxHxpnxCqqy7uzt6e3vHY9FmZocsSX0R0V1qWtkjgoj4rfxCMjOzg0XVy0clHQ28B+gsLu9uqM3M6kOWO4uXAj3AKqCQbzhmZlZrWRLB4RHxF7lHYmZm4yLL5aP/LulPJE2XdMzwK/fIzMysJrIcEewGPg38DS/cGRxAV15BmZlZ7WRJBH8BvCwinso7GDMzq70sp4b6gR15B2JmZuMjyxHBELAyvcFs1/BIXz5qZlYfsiSCWyjTK6iZmR36sjyP4PpaBGJmZuMjy53Fj1DiGcUR4auGzMzqQJZTQ8WdFB0O/CHg+wjMzOpE1auGImJz0etXEfFvwLj0PGpmZmMvy6mhuUWDTSRHCJNyi8jMzGoqy6mhfy56Pwj8EvijXKIxM7Oay3LVkJ9LYGZWx7KcGjoMeDv7Po/givzCMjOzWslyaui7wFagj6I7i83MrD5kSQQzIuL03CMxM7NxkaXTubslvSr3SMzMbFxkOSJ4A/De9A7jXYCAiIhX5xqZmZnVRJZEsCj3KMzMbNxkuXx0fS0CMTOz8ZGljWC/STpd0hpJayVdWmK6JF2VTn9wxF3MZmZWA1lODe0XSc3A54G3ABuA+yV9LyIeKiq2CDghfZ0CfDH9O+Y6L/3Bnve/vPLMPBZxSOlbv4WbVmxg2aqNPL1jAIAZRx/OK18ymSe3Pc/xUyfyyFPPcVhLEye8eBKzXzKZ1Y9v5antyRXEUycdxlGHtdC/cRuL5kznnafMpG/9Fr6zYgNPbd/F1EmH8fa5M1jzxHa+df+j7B4sMDBU4JiJbQSw8ZmdPPXsbnYNFvbt2tYOWFuzACgAx04+ghcfdRhrntzOtucHiXSDKy1XACKCtpZmZh0zgUmHt/D4MzsJoFAIntk5QGf7RM5f0Mnqx7ey9snt7BossKCrnUlHtDK/q53b+5/gtv4nOH32NC4940QAbrz3UZat3rjX/tGzbjNTJrSxZcfuPX+37xygf+M2Zk8/ihWPbuHRp3cwv6udE148ifld7QB7zTe/q33PfnVYSxOTJ7SxdUeyL5178kx+Y9oketZtZn5XOyfNmrJXHMXT1jyxnevuegQieN8buvaKcXjekcOjcSDz1qK+YorI599Q0gLg4xHxu+nwRwEi4h+LylwDLI+IJenwGmBhRGwsV293d3f09vaOKpbiJDCskZNB3/otvONLPeweLIxZnRed1sV1dz3C7qEX9qdmwZC/5euagKYmMVR44YO+6LQuZrZP5LKbV+017mv3/JLdgwUKkV5xkqHu1pYmiGCwEHvma24WgxV2rNbmJJ62libeu6CTxXeu2zOtpVkUCkFzkxgYUUdxjG0tTVx+1myuuLV/z/AN75+f+Qu4b/0W3vXlnv2aN6/6JPVFRHepaXmeGjoWeKxoeEM6brRlkHShpF5JvZs2bRrzQBtNz7rNDIxhEgC4rf+Jff6xnATqX8BeSQCSfWHZ6o37jBtOAsPzZal7YLDAwFDsNV+lJADsKT8wWOC2/if2mjY4PK1EHcUxDgwWWLZ6417DPes2Z4g60bNu837PW4v6RsozEajEuJFbP0sZIuLaiOiOiO6Ojo4xCa6Rze9qT35pjaHTZ0+jtXnvj7O51KdrdUVAc9PeH/Tps6exaM70fca1tTQxXDTLrjF8RNDarD3zNZH8qq+ktVk0K5n39NnT9prWMjytRB3DMQ7Pu2jO9L2Gh09TZTG/q32/561FfSPl1kZA8uv+uKLhGcDj+1HmgP3yyjPdRlDkpFlTWPIn88e8jeAts6e5jeAgcTC0EQB7tRG8Zfa0cWkjmNk+MXMbwXCMw/OOrCurk2ZN4Yb3zx+zc/pjXd9IebYRtAD/Dfw28CvgfuCdEdFfVOZM4GLgDJJG4qsiYl6levenjcDMrNFVaiPI7YggIgYlXQz8EGgGrouIfkkXpdMXA0tJksBaYAdwQV7xmJlZaXmeGiIilpJ82RePW1z0PoAP5hmDmZlVlusNZWZmdvBzIjAza3BOBGZmDc6JwMysweV2+WheJG0C9rdH1KnAU2MYTj3xtinP26Y8b5vyDrZtMysiSt6Re8glggMhqbfcdbSNztumPG+b8rxtyjuUto1PDZmZNTgnAjOzBtdoieDa8Q7gIOZtU563TXneNuUdMtumodoIzMxsX412RGBmZiM4EZiZNbi6TASSTpe0RtJaSZeWmC5JV6XTH5Q0dzziHA8Zts1CSVslrUxfl49HnLUm6TpJv5a0usz0Rt5nqm2bhtxnACQdJ+kOSQ9L6pf0oRJlDv59JyLq6kXS5fX/AF1AG/AA8MoRZc4AlpE8m2M+cO94x30QbZuFwK3jHes4bJvTgLnA6jLTG3KfybhtGnKfSdd9OjA3fT+J5Bksh9z3TT0eEcwD1kbEuojYDXwT+P0RZX4f+HokeoCjJU0fWVEdyrJtGlJE3Ak8XaFIo+4zWbZNw4qIjRGxIn2/HXiYfZ+7ftDvO/WYCI4FHisa3sC+H0yWMvUo63ovkPSApGWSZtcmtINeo+4zWTX8PiOpE3gdcO+ISQf9vpPrg2nGSaknW4+8RjZLmXqUZb1XkPRJ8qykM4BbgBPyDuwQ0Kj7TBYNv89IOhL4DvDhiNg2cnKJWQ6qfacejwg2AMcVDc8AHt+PMvWo6npHxLaIeDZ9vxRolTS1diEetBp1n6mq0fcZSa0kSeCGiLipRJGDft+px0RwP3CCpOMltQHnAd8bUeZ7wHvS1vz5wNaI2FjrQMdB1W0jaZokpe/nkewjm2se6cGnUfeZqhp5n0nX+yvAwxHxL2WKHfT7Tt2dGoqIQUkXAz8kuUrmuojol3RROn0xyXOUzwDWAjuAC8Yr3lrKuG3OAT4gaRDYCZwX6aUP9UzSEpKrX6ZK2gB8DGiFxt5nINO2ach9JvV64HxglaSV6bjLgJlw6Ow77mLCzKzB1eOpITMzGwUnAjOzBudEYGbW4JwIzMwanBOBmVmDcyKwXEg6WtKfjWF9CyWdOlb11TNJr03v8B1VOUm/V6pHWqt/TgSWl6OBkolAUvN+1LcQcCLI5rUk162PqlxEfC8irswpJjuIORFYXq4EXpr2T//p9Bf9HZJuBFYBSLpFUl/aj/uFwzOmz0xYkXZi9uO0M6+LgP+d1vebxQuS9HFJHykaXi2pU9JEST9I61kt6dx0+kmSfpIu+4cje4KUNFnSLyU1pcMTJD0mqVXSJZIeSvuV/2a1jSDp3ZLuS+O+RlKzpJPT+Q9PY+yXNCfdRndKujldxuKiGH5H0j3pdvl22rcNaV13p+t4n6TJwBXAuekyz5U0Ly3z8/Tvb6R3lo8s915JV6f1zkq3/YPp35np+K8p6Vv/bknrJJ0zqr3CDk7j3Q+2X/X5Ajop6r+e5Bf9c8DxReOOSf8eAawG2oEOkp4ajx9R5uPAR8osa69paV2dwNuBLxWNn0xyR+zdQEc67lySO6xH1vld4LeKynw5ff84cFj6/ugq2+BE4PtAazr8BeA96ft/AD4DfB74aNE2ep7keRHNwO0kd+1OBe4EJqbl/hq4nOSZEuuAk9PxR5H0FvBe4OqiOI4CWtL3bwa+k74fWW7PcBr3/0rfvw+4JX3/NeDbJD8iX0nSrfm4729+Hdir7rqYsIPafRHxSNHwJZLelr4/jqTHyg7gzuFyEXEg/eCvAj4j6VMkD075qaQ5wBzg9rR7nGagVL8v3yJJAHeQ9Mn0hXT8g8ANkm4h6WWzkt8GTgLuT5d1BPDrdNoVJH0/PQ9cUjTPfRGxDvZ07fCGtMwrgbvSetqAe4DfADZGxP2QdP6WzjcyjsnA9ZJOIOn1srVK3AALgLPT9/8O/FPRtFsiogA8JOnFGeqyg5wTgdXSc8NvJC0k+XW6ICJ2SFoOHE7SZe9o+z0ZZO/TnIcDRMR/SzqJ5Dz4P0r6T+BmoD8iFlSp83vpPMeQfJn/Vzr+TJIndv0e8HeSZkfEYJk6BFwfER8tMe0Y4EiSL+XDeWHbjFz3SOu5PSLesVfl0qtLlC/lE8AdEfG29DTb8gzzjFS8nF3FYexHXXaQcRuB5WU7yaP7ypkMbEmTwCtIHuEHyS/dN0o6HiD9Iq5W3y9JHqWIkufBDs/7EmBHRHyD5DTMXGAN0CFpQVqmVSUepBJJt8r3AZ8lOZoYSs/XHxcRdwB/RdIgfmSFdfwxcI6kFw2vi6RZ6bRrgb8DbgA+VTTPPCW9wzaRHJH8DOgBXi/pZWk9EyS9HPh/wEsknZyOnySppcS2mgz8Kn3/3qLxlbbp3SRHQgDvSuOwOuVEYLmIiM0kpzJWS/p0iSK3AS2SHiT5xdqTzrcJuBC4SdIDJKdoIDln/bZSjcUkfcEfo6T3xw+QPDcW4FXAfen4vwH+IZJHdJ4DfCqtfyXlr0b6FvDuohiagW9IWgX8HPjXiHhGUrekL5fYBg8Bfwv8Z7qetwPTJb0HGIyIG0ka1U+W9KZ0tnvScauBR4Cb023yXmBJWk8P8Ip0Xc4FPpeuy+0kRxd3AK8cbgQmOa3zj5LuStdh2MhyxS4BLkiXdz6wz0PZrX6491Gzg0R6uuwjEXHWOIdiDcZHBGZmDc5HBGZmDc5HBGZmDc6JwMyswTkRmJk1OCcCM7MG50RgZtbg/j+6gbUIX/IldgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CORRELATION OF TRACT UNDER-USAGE IN OTHER TRACT'S HOME DISTRICTS vs. being near a boundary\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractUse,nearEdge,marker='.' )\n",
    "ax.set(xlabel=\"tract use vs. expectation\", ylabel=\"number of unfilled wedges - max = \"+str(nWedges))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "6bb5c12a-a6a4-4cb1-9026-d227bc11c2ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.13777411338381998 = FL std dev of TRACT usage.  Here is its weighted histogram\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR tract usage in a histogram\n",
    "n_bins=50\n",
    "avgTractUse = 0.\n",
    "statePop = np.sum(tractPop)\n",
    "for t in range(nTracts):\n",
    "    avgTractUse += tractUse[t] * tractPop[t]/statePop\n",
    "sumVarTractUse = 0.\n",
    "for t in range(nTracts):\n",
    "    sumVarTractUse += (tractUse[t]-avgTractUse)**2 *tractPop[t]/statePop\n",
    "sdTractUse = sumVarTractUse ** 0.5\n",
    "\n",
    "print(sdTractUse,\"=\",STATE,\"std dev of TRACT usage.  Here is its weighted histogram\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractUse, bins=n_bins, weights=tractPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "121f9869-3d25-4594-93b3-64a57a95aca8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1412935913691295 = std dev of PRECINCT usage.  Here is its weighted histogram\n",
      "this is a histogram of PRECINCT usage by precinct for FL\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR precinct usage in a histogram  \n",
    "n_bins=50\n",
    "avgPrecinctUse = 0.\n",
    "stateVTDPop = np.sum(vtdPop)\n",
    "for p in range(nPrecincts):\n",
    "    avgPrecinctUse += precinctUse[p] * vtdPop[p]/stateVTDPop\n",
    "sumVarPrecinctUse = 0.\n",
    "for p in range(nPrecincts):\n",
    "    sumVarPrecinctUse += (precinctUse[p]-avgPrecinctUse)**2 *vtdPop[p]/stateVTDPop\n",
    "sdPrecinctUse = sumVarPrecinctUse ** 0.5\n",
    "\n",
    "print(sdPrecinctUse,\"= std dev of PRECINCT usage.  Here is its weighted histogram\")\n",
    "print(\"this is a histogram of PRECINCT usage by precinct for\",STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(precinctUse, bins=n_bins, weights=vtdPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "f6f03dcb-c722-4789-b47f-fb7aa3be97bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here's a look at numerical stability - number of loops, avg =  7.8029\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avgTLC = 0.\n",
    "usedTracts = 0.\n",
    "for t in range (nTracts):\n",
    "    if(tractLoopCounter[t] > 0):\n",
    "        avgTLC += tractLoopCounter[t]\n",
    "        usedTracts +=1.\n",
    "avgTLC = round(avgTLC / usedTracts, 4)\n",
    "print(\"here's a look at numerical stability - number of loops, avg = \",avgTLC)\n",
    "n_bins=20     \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractLoopCounter, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "fe98b798-0b94-4a44-807f-4bd15057e377",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5120978593173231 0.5067292903108906\n"
     ]
    }
   ],
   "source": [
    "stateGOP28 = 0.\n",
    "for t in range(nTracts):\n",
    "    stateGOP28 += HDweight[t]*HDvGOP[t]\n",
    "print(stateGOP28,stateGOP2)\n",
    "stateGOP2 = stateGOP28  #adding back in the panhandle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "974631b2-10ef-4847-af2e-a1e26b6fa768",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district pct GOP by tract\n",
      "statewide vote is off by -0.00485 0.5121 HD avg vs true 0.51695\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT red-blue lean in a histogram  and check the statewide vote vs. HD average\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district pct GOP by tract\") \n",
    "print(\"statewide vote is off by\",round((stateGOP2-stateGOP),5),round(stateGOP2,5),\"HD avg vs true\",round(stateGOP,5))\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvGOP, bins=n_bins,weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "720d9eb5-11e8-45f7-b402-a0048e9ddb99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculated statewide Hispanic pct was 0.25284, should have been 0.24754\n",
      "this is a histogram of home-district VAP pct Hispanic by tract\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR home district pct Hispanic in a histogram\n",
    "print(\"calculated statewide Hispanic pct was {0}, should have been {1}\"\n",
    "      .format(round(stateHisp2,5), round(np.sum(tractHisp)/np.sum(tractVAP),5) ) )\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district VAP pct Hispanic by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvHisp, bins=[0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],\n",
    "        weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "1ab7704f-a226-45a9-98a6-d0684522c1eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Hispanic for FL\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF HISPANIC VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT HISPANIC VOTE?\n",
    "n_bins = 20\n",
    "HispSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvHisp[t]*n_bins)\n",
    "    HispSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Hispanic for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,HispSeats,width=0.09 )\n",
    "#plt.scatter(binMid,HispSeats )\n",
    "ax.set(xlabel=\"pct Hispanic\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "3ef49161-368a-40a2-83ff-92187edb3462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using a threshold of 0.35 the number of Hisp districts should be 5.325\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Hispanic-heavy home districts?\n",
    "minHisp1 = 0.35\n",
    "minHisp2 = 0.40\n",
    "sumHispDistricts = 0.\n",
    "for t in range(nTracts):\n",
    "    if (HDvHisp[t] > minHisp1 and tractPop[t] > minTractPop):\n",
    "        sumHispDistricts += tractPop[t]/avgDistrictPop\n",
    "        if (HDvHisp[t] < minHisp2):\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='orange' )\n",
    "        else :            \n",
    "            plt.text(tractCPx[t],tractCPy[t],'o',color='blue', fontsize= 12)\n",
    "print(\"using a threshold of\",minHisp1,\"the number of Hisp districts should be\",round(sumHispDistricts,3))\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "a92133ad-dc95-4aa1-af02-c1d71296cee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Black for FL\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF BLACK VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT BLACK VOTE?\n",
    "n_bins = 10\n",
    "BlackSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvBlack[t]*n_bins)\n",
    "    BlackSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Black for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,BlackSeats,width=0.09 )\n",
    "ax.set(xlabel=\"pct Black\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b1e9c437-ddf7-4202-aff0-19f338a5d013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Black candidates would expect to win 4.701 GA seats out of 14\n",
      "Using simple Alabama-style of Blacks voting 0.9 Dem and whites voting 0.8 GOP\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see opportunity home districts?  #NEW FORMULATION based on ALABAMA\n",
    "isOppor = [0]*nTracts #will track whether this is an opportunity district\n",
    "blackFracSeats = 0.\n",
    "blackPctDem = 0.9  #fraction of Black voters who are Democrats (est'd from Chen / Stephanopoulos)\n",
    "whitePctGOP = 0.8   #fraction of White voters who are Republican  (\"  \")\n",
    "#minBlack1 = 0.30\n",
    "#minBlack2 = 0.50\n",
    "for t in range(nTracts):\n",
    "    pctWhiteDem = (1-whitePctGOP)*(1-HDvBlack[t])  #this is the fraction of voters who are white Democrats\n",
    "    pctBlackDem = blackPctDem * HDvBlack[t]  #fraction of voters who are black Democrats\n",
    "    if tractPop[t] > minTractPop and HDvGOP[t] < 0.5 :  #Dems would win this Home District\n",
    "        if pctBlackDem > pctWhiteDem : #Blacks would win the primary\n",
    "            isOppor[t] = 1\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "            blackFracSeats += HDweight[t]\n",
    "        else :  #the White Dem candidate would win the election          \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "print(\"Black candidates would expect to win\",round(blackFracSeats*nDistricts,3),STATE,\"seats out of\",nDistricts)\n",
    "print(\"Using simple Alabama-style of Blacks voting\",blackPctDem,\"Dem and whites voting\",whitePctGOP,\"GOP\")\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "c5125128-e4bb-4337-bc5e-3f48d7cf404f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here's the FL map with Hisp+Black greater than  0.4 or even 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Black+Hispanic heavy home districts?\n",
    "minMinor = 0.40 \n",
    "minMinor2 = 0.50\n",
    "print(\"Here's the\",STATE,\"map with Hisp+Black greater than \",minMinor,\"or even\",minMinor2)\n",
    "for t in range(nTracts):\n",
    "    if ((HDvBlack[t] + HDvHisp[t]) > minMinor and tractPop[t] > minTractPop):\n",
    "        if (HDvBlack[t] + HDvHisp[t]) > minMinor2 :\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "        else :            \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "58119ef8-13ef-4135-9843-6518025dda07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district population by tract; avg =  768212.3214285715\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT population in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district population by tract; avg = \",np.sum(tractPop)/nDistricts)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "a = avgDistrictPop\n",
    "ax.hist(HDvPop, bins=[0.9*a,0.92*a,0.95*a,0.99*a,1.0*a,1.01*a,1.05*a,1.1*a])   #n_bins\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "1f9101bd-108a-48cb-bee5-3f4e04c18132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district area by tract\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# more HOME DISTRICT STATS in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district area by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDarea, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "d8167a80-2b91-4adc-ab34-9e010838213d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of HD area to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDarea,HDvGOP,marker='.' )\n",
    "ax.set(xlabel=\"tract area\", ylabel=\"home district pct GOP\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "cf95cc7c-e58a-4174-94f6-b6d4c7ccb0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDvGOP,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"home district pct GOP\", ylabel=\"tractUse\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "617384b8-1e0b-45a7-ae09-babe8dd9d429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to tract population?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractPop,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"Census tract population\", ylabel=\"tractUse\")\n",
    "ax.set_xlim(left=0,right=10000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "91f163ab-6b94-47ee-bd29-be3e751fe2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# UPDATED VOTES-SEATS CURVE (from votes2seats.ipynb 1/15/22)\n",
    "# LET'S REWORK OUR VOTES - SEATS CALCULATIONS\n",
    "# FIRST, LET'S DO THE BASIC VOTES-TO-SEATS, no fractional seats\n",
    "# INPUTS ARE HDvGOP[nTracts] and HDweight[nTracts]\n",
    "# HDvGOP is the redness lean of each Census tract's Home District\n",
    "# HDweight is the population of this Census tract relative to the state population\n",
    "# stateGOP is the statewide fraction of GOP vote vs. GOP + Dem vote\n",
    "# KEY EQUATION:\n",
    "# expected Seats at a given vote V = sum(HDweight) for tracts with HDvGOP > 0.5 + (V - stateGOP)\n",
    "# for easy calculation, create ~1000 bins, each bin containing HD's in a dV vote window\n",
    "nBins = 1000\n",
    "dV = 1./nBins\n",
    "binWeight = [0.]*nBins\n",
    "\n",
    "for t in range(nTracts):\n",
    "    binNo = int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binWeight[binNo] += HDweight[t]\n",
    "    \n",
    "binVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "85608d3f-c3bd-4d91-9378-16270ceeb9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "cumVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    cumVote[nBins-b-1] = np.sum(binWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSeats[b] = 1. - cumVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\",\"+str(STATE), ylabel=\"GOP seats won (no fractl smearing)\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "\n",
    "#LET'S ALSO crudely ESTIMATE RESPONSIVENESS near the STATEWIDE VOTE, with a pseudonormal weighting and lst-sq fit\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=binVote[b]\n",
    "        seatData[counter]=cumSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=binVote[b]\n",
    "            seatData[counter]=cumSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsimple = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "Rx = [stateGOP - 4.*stateSigma, stateGOP + 4.*stateSigma]\n",
    "Ry = [y0 + Rx[0]*Rsimple, y0 + Rx[1]*Rsimple]\n",
    "plt.plot(Rx,Ry ) \n",
    "ax.text(Rx[1]+0.02, Ry[1]+0.02, \"R = \"+str(round(Rsimple,4)), transform=ax.transAxes, fontsize=14,color='purple')\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "ax.text(0.02,expectedSeats+0.03,\"expected seats = \"+str(round(expectedSeats,3)),transform=ax.transAxes,fontsize=9)\n",
    "ax.text(stateGOP+0.01,0.1,\"HD statewide vote = \"+str(round(stateGOP2,3)),transform=ax.transAxes,fontsize=9)\n",
    "Ex = [0,stateGOP ]\n",
    "Ey = [expectedSeats, expectedSeats]\n",
    "plt.plot(Ex,Ey, linestyle='-.',color='red')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "ce47266f-e2ea-4649-b10c-e8225994c122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3/6/22 - alternate votes-to-seats with fractional seat smearing.\n",
    "# First, compute the general cdf sliver for -3 sigma to +3 sigma\n",
    "# Then, apply this smearing to the binVote weights\n",
    "seatVar = 0.04\n",
    "nBins = 1000\n",
    "nSigma = 6.  #how many total sigma of deviation we are explicitly modeling; the rest go into the tails\n",
    "nSlivers = 2*int(3*nBins*seatVar)  #budget for 3 sigma on either side of the expected vote\n",
    "sliverWt = [0.]*nSlivers  #each sliver holds the cdf reflecting the relative distance from the mean vote\n",
    "sliverSigma = [0.]*nSlivers\n",
    "totalSliverWt = 0.\n",
    "for nS in range(nSlivers):\n",
    "    dSigmaHigh = nSigma* (nS+0.5 - nSlivers/2) / float(nSlivers)\n",
    "    dSigmaLow =  nSigma* (nS-0.5 - nSlivers/2) / float(nSlivers)\n",
    "    sliverSigma[nS] = 0.5*(dSigmaHigh+dSigmaLow)  #for plotting; keeps track of this sliver's center sigma\n",
    "    sliverWt[nS] = norm.cdf(dSigmaHigh) - norm.cdf(dSigmaLow)\n",
    "    totalSliverWt += sliverWt[nS]\n",
    "lowSliverWt = 0.5 * (1. - totalSliverWt)\n",
    "highSliverWt = lowSliverWt\n",
    "#print(\"each tail of distribution has weight\",round(lowSliverWt,4) )\n",
    "#plt.plot(sliverSigma,sliverWt)\n",
    "#plt.show()\n",
    "# OK, that worked as expected.  Now, do the smearing of each bin\n",
    "smearedBinWeight = [0.]*nBins\n",
    "for b in range(nBins):\n",
    "    centerBinNo = b #int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binOffset = int(nSlivers/2)\n",
    "    for nS in range(nSlivers):\n",
    "        binNo = centerBinNo + nS - binOffset\n",
    "        if (binNo < 0): #deep blue district; variance would push vote %R < 0\n",
    "            binNo = 0\n",
    "        if (binNo >= nBins):  #deep red district; variance would push vote %R > 1\n",
    "            binNo = nBins - 1\n",
    "        smearedBinWeight[binNo] += binWeight[b]*sliverWt[nS]\n",
    "    # Now put the tails into the correct bins\n",
    "    binNo = centerBinNo -1 - binOffset  #left tail\n",
    "    if binNo < 0:\n",
    "        binNo = 0\n",
    "    smearedBinWeight[binNo] += binWeight[b]*lowSliverWt\n",
    "    binNo = centerBinNo + nSlivers - binOffset  #right tail\n",
    "    if binNo >= nBins:\n",
    "        binNo = nBins -1 \n",
    "    smearedBinWeight[binNo] += binWeight[b]*highSliverWt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\",label=\"unsmeared\")\n",
    "plt.plot(binVote, smearedBinWeight, marker='o',linestyle=\"none\",label=\"smeared\")\n",
    "RANGE = [0.0, 0.5*np.max(binWeight)]\n",
    "sGOP = [stateGOP, stateGOP]\n",
    "plt.plot(sGOP,RANGE,linestyle=\"-\",label=\"statewide \"+str(round(stateGOP,3)) )\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.legend()\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "8a2d7de3-ea93-4447-bb69-36defbae38df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "smearedBinVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "cumSmearedVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    smearedBinVote[b] = b*dV\n",
    "    cumSmearedVote[nBins-b-1] = np.sum(smearedBinWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSmearedSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSmearedSeats[b] = 1. - cumSmearedVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(smearedBinVote,cumSmearedSeats, marker='o',linestyle=\"none\",label=str(seatVar)+\" smear\")\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\",label=\"no smear\")\n",
    "ax.set(xlabel=\"true \"+STATE+\"-wide vote=\"+str(round(stateGOP,4))+\", nBins =\"+str(nBins), ylabel=\"GOP seats won\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "expectedS = [expectedSeats,expectedSeats]\n",
    "smearedSeats = cumSmearedSeats[stateVoteBin]\n",
    "smearedS = [smearedSeats,smearedSeats]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(RANGE,smearedS, linestyle=\"--\",color='blue',label=str(round(expectedSeats,3))+\"no smear\")\n",
    "plt.plot(RANGE,expectedS, linestyle=\"--\",color='orange',label=str(round(smearedSeats,3))+\"smeared\")\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "597b7758-4f83-40f7-8909-4946e64267ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simpler and fractional-seat-smeared (var of 0.04 ) responsiveness are 2.959 3.143\n",
      "fractional expected GOP seats =  16.487  out of  28 seats for FL\n",
      "simpler expected GOP seats =  16.7015  out of  28 seats\n"
     ]
    }
   ],
   "source": [
    "#Finally, let's compare responsiveness using smeared (fractional seat variance) to non-smeared calculated above\n",
    "#LET'S ALSO crudely ESTIMATE (smeared) RESPONSIVENESS near the STATEWIDE VOTE, as we did for non-smeared\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=smearedBinVote[b]\n",
    "        seatData[counter]=cumSmearedSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=smearedBinVote[b]\n",
    "            seatData[counter]=cumSmearedSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsmeared = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "\n",
    "print(\"simpler and fractional-seat-smeared (var of\",seatVar,\") responsiveness are\",round(Rsimple,3) ,round(Rsmeared,3) )   \n",
    "print(\"fractional expected GOP seats = \",round(nDistricts*smearedSeats,3),\" out of \",nDistricts,\"seats for\",STATE)\n",
    "print(\"simpler expected GOP seats = \",round(nDistricts*expectedSeats,4),\" out of \",nDistricts,\"seats\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86f82727-7193-4194-aa61-2b99408dbcb6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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